Optimizing a complex system arising from physics or engineering covers a vast spectrum in basic and applied sciences. Although we target a certain transversality from analysis to implementation, the particular fields in which we are trying to excell can be defined more precisely.

From the *physical analysis* point of view,
our expertise relies mostly on
Fluid and Structural Mechanics and Electromagnetics. In the former project
Sinus, some of us had contributed to the basic understanding of fluid
mechanical phenomena (Combustion, Hypersonic Non-Equilibrium Flow,
Turbulence). More emphasis is now given to the coupling of
engineering disciplines and to the validation of corresponding numerical
methodologies.

From the *mathematical analysis* point of view, we are concerned with
functional analysis related to partial-differential equations, and the
functional/algebraic analysis of numerical algorithms.
Identifying the Sobolev space in which the direct or the inverse problem
makes sound sense, tailoring the numerical method to it,
identifying a functional gradient
in a continuous or discrete setting, analyzing iterative convergence, improving
it, measuring multi-disciplinary coupling strength and identifying critical
numerical parameters, etc constitute a non-exhaustive list of mathematical
problems we are concerned with.

Regarding more specifically the
*numerical aspects* (for the simulation of PDEs), considerable
developments have been achieved by the scientific community at large, in
recent years. The areas with the closest links with our research are:

*approximation schemes*, particularly by the introduction of
specialized Riemann solvers for complex hyperbolic systems in
Finite-Volume/Finite-Element formulations, and
highly-accurate approximations (e.g. ENO schemes),

*solution algorithms*, particularly by the multigrid, or multilevel
and multi-domain algorithms best-equiped to overcome numerical stiffness,

*parallel implementation and software platforms*.

After contributing to some of these progresses in the former project Sinus,
we are trying to extend the numerical approach to a more global one,
including
an optimization loop, and thus contribute, in the long-term, to modern
scientific computing and engineering design. We are currently dealing mostly
with *geometrical optimization*.

*Software platforms* are perceived as a necessary component to actually
achieve the computational cost-efficiency and versatility necessary to master
multi-disciplinary couplings required today by size engineering simulations.

The project has several objectives: to analyze mathematically coupled PDE systems involving one or more disciplines in the perspective of geometrical optimization or control, to construct, analyze and experiment numerical algorithms for the efficient solution of PDEs (coupling algorithms, model reduction), or multi-criterion optimization of discretized PDEs (gradient-based methods, evolutionary algorithms, hybrid methods, artificial neural networks, game strategies), to develop software platforms for code-coupling and for parallel and distributed computing.

Major applications include : the multi-disciplinary optimization of
aerodynamic configurations (wings in particular) in partnership
with the French or European aeronautical industry and research (Airbus, Dassault Aviation,
ONERA, etc), the geometrical optimization of antennas in partnership with France Télécom and Thalès
Air Défense (see Opratel Virtual Lab.), the development of *Collaborative,
Distributed and High-Performance environments in collaboration with Chinese partners (CAE)*.

Our common scientific background is the functional and numerical analysis of PDE systems, in particular with respect to nonlinear hyperbolic equations such as conservation laws of gas-dynamics.

Whereas the structure of weak solutions of the Euler equations has been thoroughly discussed in both the mathematical and fluid mechanics literature, in similar hyperbolic models, focus of new interest, such as those related to traffic, the situation is not so well established, except in one space dimension, and scalar equations. Thus, the study of such equations is one theme of emphasis of our research.

The well-developed domain of numerical methods for PDE systems, in particular finite volumes, constitute the sound background for PDE-constrained optimization.

Partial Differential Equations (PDEs), finite volumes/elements, geometrical optimization, optimum shape design, multi-point/multi-criterion/multi-disciplinary optimization, shape parameterization, gradient-based/evolutionary/hybrid optimizers, hierarchical physical/numerical models, Proper Orthogonal Decomposition (POD)

Optimization problems involving systems governed by PDEs, such as optimum shape design in aerodynamics or electromagnetics, are more and more complex in the industrial setting.

In certain situations, the major difficulty resides in the costly evaluation of a functional by means of a simulation, and the numerical method to be used must exploit at best the problem characteristics (regularity or smoothness, local convexity).

In many other cases, several criteria are to be optimized and some are
non differentiable and/or non convex. A large set of parameters,
sometimes of different types (boolean, integer, real or functional),
are to be taken into account, as well as constraints of various types
(physical and geometrical, in particular).
Additionally, today's most interesting optimization pre-industrial
projects are multi-disciplinary, and this complicates the
mathematical, physical and numerical settings.
Developing *robust optimizers* is therefore an essential
objective to make progress in this area of scientific computing.

In the area of numerical optimization algorithms, the project aims at adapting classical optimization methods (simplex, gradient, quasi-Newton) when applicable to relevant engineering applications, as well as developing and testing less conventional approaches such as Evolutionary Strategies (ES), including Genetic or Particle-Swarm Algorithms, or hybrid schemes, in contexts where robustness is a very severe constraint.

In a different perspective, the heritage from the former project Sinus in Finite-Volumes (or -Elements) for nonlinear hyperbolic problems, leads us to examine cost-efficiency issues of large shape-optimization applications with an emphasis on the PDE approximation; of particular interest to us:

best approximation and shape-parameterization,

convergence acceleration (in particular by multi-level methods),

model reduction (e.g. by *Proper Orthogonal Decomposition*),

parallel and grid computing; etc.

Jean-Paul Zolesio and Michel Delfour have developed, in particular in their book , a theoretical framework for for geometrical optimization and shape control in Sobolev spaces.

In preparation to the construction of sound numerical techniques, their contribution remains a fundamental building block for the functional analysis of shape optimization formulations.

Developing grid, cloud and high-performance computing for complex applications is one of the priorities of the IST chapter in the 7th Framework Program of the European Community. One of the challenges of the 21st century in the computer science area lies in the integration of various expertise in complex application areas such as simulation and optimization in aeronautics, automotive and nuclear simulation. Indeed, the design of the reentry vehicle of a space shuttle calls for aerothermal, aerostructure and aerodynamics disciplines which all interact in hypersonic regime, together with electromagnetics. Further, efficient, reliable, and safe design of aircraft involve thermal flows analysis, consumption optimization, noise reduction for environmental safety, using for example aeroacoustics expertise.

The integration of such various disciplines requires powerful computing infrastructures and particular software coupling techniques. Simultaneously, advances in computer technology militate in favor of the use of massively parallel clusters including hundreds of thousands of processors connected by high-speed gigabits/sec networks. This conjunction makes it possible for an unprecedented cross-fertilization of computational methods and computer science. New approaches including evolutionary algorithms, parameterization, multi-hierarchical decomposition lend themselves seamlessly to parallel implementations in such computing infrastructures. This opportunity is being dealt with by the Opale project-team since its very beginning. A software integration platform has been designed by the Opale project-team for the definition, configuration and deployment of multidisciplinary applications on a distributed heterogeneous infrastructure. Experiments conducted within European projects and industrial cooperations using CAST have led to significant performance results in complex aerodynamics optimization test-cases involving multi-elements airfoils and evolutionary algorithms, i.e. coupling genetic and hierarchical algorithms involving game strategies .

The main difficulty still remains however in the deployment and control of complex distributed applications by the end-users. Indeed, the deployment of the computing infrastructures and of the applications in such environments still requires specific expertise by computer science specialists. However, the users, which are experts in their particular application fields, e.g. aerodynamics, are not necessarily experts in distributed and grid computing. Being accustomed to Internet browsers, they want similar interfaces to interact with high-performance computing and problem-solving environments. A first approach to solve this problem is to define component-based infrastructures, e.g. the Corba Component Model, where the applications are considered as connection networks including various application codes. The advantage is here to implement a uniform approach for both the underlying infrastructure and the application modules. However, it still requires specific expertise not directly related to the application domains of each particular user. A second approach is to make use of web services, defined as application and support procedures to standardize access and invocation to remote support and application codes. This is usually considered as an extension of Web services to distributed infrastructures. A new approach, which is currently being explored by the Opale project, is the design of a virtual computing environment able to hide the underlying high-performance-computing infrastructures to the users. The team is exploring the use of distributed workflows to define, monitor and control the execution of high-performance simulations on distributed clusters. The platform includes resilience, i.e., fault-tolerance features allowing for resource demanding and erroneous applications to be dynamically restarted safely, without user intervention.

The demand of the aeronautical industry remains very strong in aerodynamics, as much for conventional aircraft, whose performance must be enhanced to meet new societal requirements in terms of economy, noise (particularly during landing), vortex production near runways, etc., as for high-capacity or supersonic aircraft of the future. Our implication concerns shape optimization of wings or simplified configurations.

Our current involvement with Space applications relates to software platforms for code coupling.

A new application domain related to the parameter and shape optimization of mechanical structures is under active development. The mechanical models range from linear elasticity of 2D or 3D structures, or thin shells, to nonlinear elastoplasticity and structural dynamics. The criteria under consideration are multiple: formability, stiffness, rupture, fatigue, crash, and so on. The design variables are the thickness and shape, and possibly the topology, of the structures. The applications are performed in collaboration with world-leading industrials, and involve the optimization of the stamping process (Blank Force, Die and Tools shapes) of High Performance steel structures as well as the optimal design of structures used for packaging purposes (cans and sprays under high pressure). Our main contribution relies on providing original and efficient algorithms to capture Pareto fronts, using smart meta-modelling, and to apply game theory approaches and algorithms to propose stable compromise solutions (e.g. Nash equilibria).

In the context of shape optimization of antennas, we can split the existing results in two parts: the two-dimensional modeling concerning only the specific transverse mode TE or TM, and treatments of the real physical 3-D propagation accounting for no particular symmetry, whose objective is to optimize and identify real objects such as antennas.

Most of the numerical literature in shape
optimization in electromagnetics belongs to the first part and makes
intensive use of the 2-D solvers based on the specific 2-D Green
kernels. The 2-D approach for the optimization of *directivity*
led recently to serious errors due to the modeling
defect. There is definitely little hope for extending the 2-D
algorithms to real situations. Our approach relies on a full
analysis in unbounded domains of shape sensitivity analysis for the
Maxwell equations (in the time-dependent or harmonic formulation),
in particular, by using the integral formulation and the
variations of the Colton and Kreiss isomorphism. The use of the
France Telecom software SR3D enables us to directly implement our
shape sensitivity analysis in the harmonic approach.
This technique makes it possible,
with an adequate interpolation, to retrieve the shape
derivatives from the physical vector fields in the time evolution
processes involving initial impulses, such as
radar or tomography devices, etc. Our approach is
complementary to the “automatic differentiation codes” which are
also very powerful in many areas of computational sciences. In
Electromagnetics, the analysis of hyperbolic equations requires a sound
treatment and a clear understanding of the influence of space
approximation.

A particular effort is made to apply our expertise in solid and fluid mechanics, shape and topology design, multidisciplinary optimization by game strategies to biology and medicine. We focus more precisely on developing and validating cell dynamics models. Two selected applications are privileged: solid tumors and wound healing.

Opale's objective is to push further the investigation of these applications, from a mathematical-theoretical viewpoint and from a computational and software development viewpoint as well. These studies are led in collaboration with biologists, as well as image processing specialists.

The modeling and analysis of traffic phenomena can be performed at a macroscopic scale
by using partial differential equations derived from fluid dynamics. Such models give a description
of collective dynamics in terms of the spatial density

Opale's research focuses on the study of macroscopic models of vehicular and pedestrian traffic, and how optimal control approaches can be used in traffic management. The project opens new perspectives of inter-disciplinary collaborations on urban planning and crowd dynamics analysis.

Our expertise in theoretical and numerical modeling, in particular in relation to approximation schemes, and multilevel, multi-scale computational algorithms, allows us to envisage to contribute to integrated projects focused on disciplines other than, or coupled with fluid dynamics, such as structural mechanics, electromagnetics, biology and virtual reality, image processing, etc in collaboration with specialists of these fields. Part of this research is conducted in collaboration with ONERA.

The Opale project-team has initiated a few years ago the development of
NUM3SIS (http://

NUM3SIS provides innovative software tools to overcome some limitations encountered by classical monolithic simulation codes. In particular, the platform is based on abstract concepts commonly used in scientific computing, such as mesh, fields, finite-elements, linear solvers etc, that can be implemented in plugins. A fast prototyping of algorithms can be achieved using a visual programing interface. A component is dedicated to deployment on parallel architectures. Moreover, the platform relies on a "store" system to foster exchange of plugins, scripts or data.

This work is being carried out with the support of one engineer in the framework of an ADT (Action de Développement Technologique) program.

Opale team is developing the software platform FAMOSA (C++), that is devoted to multidisciplinary design optimization in engineering. It integrates the following components:

an optimization library composed of various algorithms : several descent methods from steepest-descent method to quasi-Newton BFGS method (deterministic, smooth), the Multi-directional Search Algorithm (deterministic, noisy), the Covariance Matrix Adaption Evolution Strategy (semi-stochastic, multi-modal) and the Efficient Global Optimization method (deterministic, multi-modal). It also contains the Pareto Archived Evolution Strategy to solve multi-objective optimization problems ;

an evaluation library managing the performance estimation process (communication with external simulation tools) ;

a metamodel library that contains tools to build a database and kriging models that are used to approximate the objective function for different purposes;

a scenario library that allows to use the previous components to achieve various tasks:

Construct a design of experiments ;

Construct a metamodel ;

Find the design that minimizes a cost functional ;

Find the Pareto front for two cost functionals

Play a Nash game to find the equilibrium between two criteria ;

Apply a multiple gradient descent strategy to improve simultaneously two criteria.

The FAMOSA platform is employed by Opale project-team to test its methodological developments. The platform is also used by the Fluid Mechanics Laboratory at Ecole Centrale de Nantes for hydrodynamic design applications and ONERA for multidisciplinary design optimization (MDO). Moreover, it is presently tested by Peugeot Automotive industry for external aerodynamic design purpose.

Opale team is developing plugins in the framework of the algebraic modeler Axel, in collaboration with the Galaad project-team. These developments correspond to two research axes :

isogeometric analysis and design. In particular, two simulation tools for heat conduction and compressible flows have been implemented, in conjunction with some deterministic and semi-stochastic optimization algorithms for optimum-shape design ;

geometrical modeling for design optimization.

A prototype software integration platform is developed and tested for multidiscipline optimization applications. It is based
on a workflow management system called YAWL (http://

Paola Goatin was awarded the *“Prix Inria - Académie des sciences du jeune chercheur”*.

In collaboration with UC Berkeley, and as part of the Associated Team ORESTE activity (see http://

As part of our TRAM3 activity and in collaboration with C. Chalons (UVSQ), we designed a new finite volume conservative algorithm to track the trajectory of a bus in the surrounding traffic using a locally non-uniform moving mesh, see .

In collaboration with S. Blandin (IBM), we proved the existence and stability of entropy weak solutions of a scalar conservation law with non-local flux arising in traffic flow modeling. The result is obtained providing accurate

In collaboration with the University of Mannheim and in the framework of the PHC Procope project “Transport Networks Modeling and Analysis”, we studied how to manage variable speed limits combined with coordinated ramp metering within the framework of the LWR network model. Following a "first discretize then optimize" approach, we derived the first order optimality system and explained the switch of speeds at certain fixed points in time and the boundary control for the ramp metering as well. Sequential quadratic programming methods are used to solve the control problem numerically. For application purposes, we present experimental setups where variable speed limits are used as a traffic guidance system to avoid traffic jams on highway interchanges and on-ramps, see .

Finally, E. Bertino internship was devoted to uncertainty quantification in macroscopic traffic flow models.

A. Aggarwal postdoc is devoted to the analytical and numerical study of systems of conservation laws with non-local fluxes in several space dimensions.
In collaboration with R.M. Colombo, we presented a Lax-Friedrichs type algorithm to numerically integrate this class of systems. The convergence of the approximate solutions was proved, also providing the existence of solution in a slightly more general setting than in other results in the current literature. An application to a crowd dynamics model is considered. This numerical algorithm is then used to test the conjecture that as the convolution kernels converge to a Dirac

M. Mimault is working on scalar conservation laws with non-local flow in two space dimensions. These equations are meant to model crowd motion, where the movement direction of each pedestrian depends on a weighted mean of the crowd density around him. In particular, M. Mimault is implementing a finite volume numerical scheme which will be used for flow optimization purposes.

The above researches were partially funded by the ERC Starting Grant "TRAM3 - Traffic management by macroscopic models".

In computational sciences for physics and engineering, Computational Fluid Dynamics (CFD) are playing one of the major roles in the scientific community to foster innovative developments of numerical methodologies. Very naturally, our expertise in compressible CFD has led us to give our research on numerical strategies for optimum design a particular, but not exclusive focus on fluids.

The framework of our research aims to contribute to numerical strategies for PDE-constrained multi-objective optimization, with a particular emphasis on CPU-demanding computational applications in which the different criteria to be minimized (or reduced) originate from different physical disciplines that share the same set of design variables. These disciplines are often fluids, as a primary focus, coupled with some other disciplines, such as structural mechanics.

Our approach to *competitive optimization* is focused on the two-discipline problem.
It is based on a particular construction of *Nash games*,
relying on a *split of territory* in the assignment of individual strategies.
A methodology has been proposed for the
treatment of two-discipline optimization problems in which one discipline,
the primary discipline, is preponderant, or fragile. Then, it is recommended
to identify, in a first step, the optimum of this discipline alone using the
whole set of design variables. Then, an orthogonal basis is constructed based
on the evaluation at convergence of the Hessian matrix of the primary
criterion and constraint gradients. This basis is used to split the working design
space into two supplementary subspaces to be assigned, in a second step, to
two virtual players in competition
in an adapted Nash game, devised to reduce a secondary
criterion while causing the least degradation to the first. The formulation has been
proved to potentially provide a set of Nash equilibrium solutions originating
from the original single-discipline optimum point by smooth continuation, thus
introducing competition gradually
(see also subsectionsubsect:helico).

Our approach to *cooperative optimization*, in theory, is not limited in number of objective functions.
It is based on a result of convex analysis established for a general unconstrained
multiobjective problem in which all the gradients are assumed to be known.
The theorem
states that in the convex hull of the gradients, there exists a
unique vector of minimal norm,

The two approaches have been combined to explore the Pareto front segment-wise as illustrated on Figure .

In multi-objective optimization, the knowledge of the Pareto set provides valuable information on the reachable optimal performance. A number of evolutionary strategies (PAES, NSGA-II, etc), have been proposed in the literature and proved to be successful to identify the Pareto set. However, these derivative-free algorithms are very demanding in terms of computational time. Today, in many areas of computational sciences, codes are developed that include the calculation of the gradient, cautiously validated and calibrated .

The notion of Pareto-stationarity, originally established to be a necessary condition of optimality in differentiable multi-objective optimization of unconstrained problems, has been extended to problems subject to equality constraints. On this basis, we were able to establish that by augmenting, in a classical manner, the objective-functions of a penalty term equal to the square of the constraint violation, and applying the standard MGDA to it, would result in converged solutions that are Pareto-stationary in the extended sense. Numerical experimentation on this is on-going.

Using MGDA in a multi objective optimization problem requires the evaluation of a large number of points with regard to criteria, and their gradients. In the particular case of a CFD problems, each point evaluation is very costly since it involves a flow computation, possibly the solution of an adjoint-equation. To alleviate this difficulty, we have proposed to construct meta-models of the functionals of interest (lift, drag, etc) and to calculate approximate gradients by local finite differences. These meta-models are updated throughout the convergence process to the evaluation of the new design points by the high-fidelity model, here the 3D compressible Euler equations.

This variant of MGDA has been tested successfully over several aerodynamic shape optimization problems : lift concurrently with drag optimization for transonic aircraft; drag (under lift constraint) concurrently with sonic boom reduction for 3D supersonic configuration (at ONERA); drag (under lift constraint) concurrently with mass reduction for transport aircraft (at ONERA) .

MGDA has successfully been tested over a two-objective optimization problem governed by two-dimensional elasticity. The deformation of a plate is calculated using an isogeometric approximation and compliance derived from it. The exact parametric shape gradient is calculated, yielding the gradient of the objective function in two antagonistic situations differing by the loading. Pareto-fronts are thus identified .

An approach has been developed to solve optimization problems in which the functional that has to be minimized is time dependent. In the literature, the most common approach when dealing with unsteady problems, is to consider a time-average criterion. However, this approach is limited since the dynamical nature of the state is neglected. Our alternative consists in building a set of cost functionals by evaluating a single criterion at different sampling times. In this way, the optimization of the unsteady system is formulated as a multi-objective optimization problem, solved using an appropriate descent algorithm (MGDA). Moreover, we also consider a hybrid approach in which the set of cost functionals is built by doing a time-average operation over multiple intervals. These strategies have been illustrated and applied to a non-linear unsteady system governed by a one-dimensional convection-diffusion-reaction partial differential equation .

MGDA offers the possibility to handle in a rational way several objective-functions for which gradients are known or approximated concurrently. This potential opens methodological paths to several themes of interest in high-fidelity simulation-based optimization: optimization of complex systems whose performance is evaluated w.r.t. several criteria originating from different, coupled disciplines; optimization under uncertainties, by introducing sensitivities as additional objectives; optimization of time-dependent systems, such as optimization of flow-control devices that generate a periodic flow (see next subsection), by converting the problem into a multipoint problem by time-discretization of the time and parameter-dependent functional (as above); etc.

Shape optimization methods are not efficient to improve the performance of fluid systems, when the flow is characterized by a strong unsteadiness related to a massive detachment. This is typically the case for the flow around an automotive body or a wing in stall condition. To overcome this difficulty, flow control strategies are developed, that aim at manipulating vortex dynamics by introducing some active actuators, such as periodic blowing/suction jets. In this context, the choice of the control parameters (location, amplitude, frequency) is critical and not straightforward. Therefore, we develop a methodology to determine optimal control parameters by coupling the simulation of unsteady actuated flows with optimization algorithms. Two research axes have been considered :

the resolution of the unsteady sensitivity equations derived from the state equations, to exhibit the dependency of the flow dynamics with respect to the control and apply an unsteady gradient-based approach ;

the optimization of control parameters using a statistical metamodel-based strategy .

In this perspective, unsteady Reynolds Averaged Navier-Stokes equations are solved, with some turbulence closures. Different models for synthetic jet have been implemented to simulate the actuation, and then validated for different turbulence closures .

Specific developments have be carried out in the metamodel-based optimizer to include a noise term into Gaussian Process model, which is used to filter errors arising from unsteady simulations. A systematic assessment of modeling and numerical errors has been archived for a backward facing step test-case, with the objective of controlling the re-attachment point location, .

This activity is conducted in collaboration with the CFD team of Ecole Centrale de Nantes.

In his doctoral thesis , Nguyen Dinh has studied mesh adaptation methods based on the total derivatives of aerodynamic outputs with respect to mesh coordinates by the discrete adjoint method. Firstly, mesh adaptation methods have been devised for Eulerian flows. Zones to be refined are detected using a sensor based on the total derivative, and numerical experiments confirmed the adequacy of the approach. Secondly, the method was extended to the Reynolds-averaged Navier equations (RANS) and thirdly demonstrated for 3D industrial configurations .

E. Roca Leon has conducted a CIFRE thesis at ONERA DAAP supported by Airbus Helicopter (Marignane) . This thesis follows the doctoral thesis of A. Dumont in which the adjoint-equation approach was used to optimize a rotor blade in hovering flight. The goal of this new thesis is to solve a two-objective optimization problem in which the hovering-flight criterion is considered preponderant, but a new criterion that takes into account the forward-flight situation is also introduced, concurrently. The second criterion is the power necessary to maintain the forward motion. The first phase of thesis work has been devoted to the set up of a hierarchy of models from low to high fidelity, in order to calibrate appropriate functional criteria. Then, actual two-objective optimizations are conducted via our Nash game approach to competitive optimization with territory splitting based on reduced Hessian diagonalization. Successful optimization has been realized involving 16 geometrical parameters to reduce the power in forward motion while maintaining sub-optimality of the drag in hover .

Design optimization stands at the crossroad of different scientific fields (and related software): Computer-Aided Design (CAD), Computational Fluid Dynamics (CFD) or Computational Structural Dynamics (CSM), parametric optimization. However, these different fields are usually not based on the same geometrical representations. CAD software relies on Splines or NURBS representations, CFD and CSM software uses grid-based geometric descriptions (structured or unstructured), optimization algorithms handle specific shape parameters. Therefore, in conventional approaches, several information transfers occur during the design phase, yielding approximations that can significantly deteriorate the overall efficiency of the design optimization procedure. Moreover, software coupling is often cumbersome in this context.

The isogeometric approach proposes to definitely overcome this difficulty by using CAD standards as a unique representation for all disciplines. The isogeometric analysis consists in developing methods that use NURBS representations for geometric modeling, computational domain description and solution basis functions. Using such a unique data structure allows to compute the solution on the exact geometry (not a discretized geometry), obtain a more accurate solution (high-order approximation), reduce spurious numerical sources of noise that deteriorate convergence, avoid data transfers between the software. Moreover, NURBS representations are naturally hierarchical and allows to define multi-level algorithms for solvers as well as optimizers.

In this context, a collaborative work has also been carried out with the Technical University of Kaiserslautern, concerning the computation of shape gradients for linear elasticity problems, . Moreover, the doctoral thesis of Asma Gdhami, in collaboration with ENIT in Tunisia, has started and concerns the development of isogeometric schemes for hyperbolic systems.

In collaboration with the ArcelorMittal's Center for Research in Automotive and Applications (CRAA), we study the multidisciplinary shape and parameter design of highly non linear mechanical 2D and 3D structures. We have developed methods adapted to the approximation of Pareto Fronts such as Normal Boundary Intersection NBI and Normalized Normal Constraint Method NNCM. Due to the time consuming cost evaluation, the use of cheap to evaluate surrogate models is mandatory. We have studied the consistency of the approach NBI or NNCM plus surrogates, which turned out to be successful for a broad panel of standard mathematical benchmarks. The coupling is successfully applied to a small scale industrial case, namely the shape optimization of a can bottom vis à vis dome reversal pressure and dome growth criteria. We have then defined a Nash game between criteria where the latter are approximated by the RBF metamodels. First, we validated the computation of a Nash equilibrium for mathematical functions, then we computed Nash equilibria for the small scale industrial case of the shape optimization of the can bottom.

Then, we considered the 3D problem of an automotive twist beam. In this 3D case, we aim to Pareto-optimal shapes for two objectives, the first being to minimize the Von-Mises strain to guarantee the formability of the twist beam, and the second being to maximize the stiffness. For solution with higher stiffness than the initial one, we could decrease the thickness to obtain a mass reduction with the same end-user properties.

We also introduced, to our knowledge for the first time in the structural optimization area, the notion of Kalai-Smorodinky equilibria which is aimed at the selection of equilibria among Pareto-optimal solutions. We applied this notion of equilibria to both industrial cases, and compared the results to Nash equilibria.

Within the OASIS Consortium (ArcelorMittal, ErDF, Inria, UTC, EURODECISION, ESILV, NECS, DeltaCAD, SCILAB-DIGITEO), Opale Project leads the Optimization task. Our aim is to develop decentralized decision-making algorithms dedicated to find efficient solutions (Pareto optimal) in a complex multidisciplinary framework (forming, stamping, welding non-linear processes, spring-back, vibration, in-function linear processes, crash and fatigue non linear and non differentiable processes) for several (between three and five) criteria. An important difficulty when trying to identify the Pareto Front, even when using adapted methods such the Normal Boundary Intersection, is that the criteria involved (thanks to the high nonlinearity in the mechanical models) exhibit many local optima. So one must use global optimization methods. We have studied the hybrid approach Simulated Annealing with Simultaneous Perturbation SASP for a suite of mathematical test-cases. To envisage the application of our method to the complex CPU time consuming stamping process, we lead an intermediate phase dedicated to the validation of the SASP method for the minimization of the spring-back that follows the stamping of a metal sheet, the design variable being the process parameters (two then four parameters). Then, we considered the more complex shape design of the initial blank. The initial blank design is a critical step in stamping design procedure, therefore it should be optimally designed. Our aim is to find the optimal initial blank shape that avoids or at least minimizes the springback and failure flaws. For this study, the geometry of the blank contour is described by parametric spline curves. Seven control points (P1,...,P7) are used to define the spline curves in order to have a wide variety of geometries. The exact computational evaluation of our criteria, springback and failure, is very expensive (the FE model request around 45 min to predict these two criteria) and the design space is of quite high dimension. Therefore, we considered the recourse to the sparse grid interpolation. Optimization process based on sparse grid interpolation is an optimal alternative in which criteria can be approximated with a suitable interpolation formula that needs significantly less points than the full grid. the obtained metamodel using sparse grid interpolation needs less than 1s to predict springback and failure on the same computation machine. To find the optimal initial blank shape, it was decided to perform the optimization process using the obtained metamodel. The construction of the sparse grid interpolant was based on the Chebyshev Gauss-Lobatto grid type and using the polynomial basis functions. This technique achieves a good accuracy with a competitive number of grid points. The comparison of the obtained fronts shows that we can capture Pareto solutions by NBI and NNCM with fewer points than NSGAII which requires a large number of populations and several generations to obtain the Pareto front.

We address in this joint collaboration the ability of the widely used Fisher-KPP equations to render some of the dynamical features of epithelial cell-sheets during wound closure.

Our approach is based on nonlinear parameter identification, in a two-dimensional setting, and using advanced 2D image processing of the video acquired sequences. As original contribution, we lead a detailed study of the profiles of the classically used cost functions, and we address the "wound constant speed" assumption, showing that it should be handled with care.

We study five MDCK cell monolayer assays in a reference, activated and inhibited migration conditions. Modulo the inherent variability of biological assays, we show that in the assay where migration is not exogeneously activated or inhibited, the wound velocity is constant. The Fisher-KPP equation is able to accurately predict, until the final closure of the wound, the evolution of the wound area, the mean velocity of the cell front, and the time at which the closure occurred. We also show that for activated as well as for inhibited migration assays, many of the cell-sheet dynamics cannot be well captured by the Fisher-KPP model. Original unexplored utilizations of the model such as wound assays classification based on the calibrated diffusion and proliferation rate parameters is ongoing.

The work carried in previous years on distributed High-Performance Computing for e-Science workflows has enlightened the need for appropriate tools and methods to manage petabyte and exabyte volumes of data. This has been the focus of the work carried by Benoit Lange during his Post-Doc position in 2014. It was dedicated to the definition and prototyping of a large-scale HPC platform to support the execution of application solvers, efficient storage and management of large-volumes of data produced by the simulation applications and the visualization of their results on high-end graphics workstations. This platform also includes analytics software to produce specific results corresponding to the user queries. This is based on the Hadoop ecosystem . Is is central for the communication between the dedicated HPC nodes running the solvers and the visualization nodes interfacing the end-users. It includes high-speed storage with dedicated file systems on specific nodes, and long-term storage for reference data using magnetic juke-boxes that store petabytes of application data. This work is supported by an FP7 project in which Inria is responsible for the Data Management work-package (Call FP7-2013-ICT-11, Grant 619439, 2014-2016). The partners of the project, named VELaSSCo (Visualization for Extremely Large Scale Scientific Computing), are : CIMNE (SP, coordinator), JOTNE and SINTEF (No), ATOS (SP), Fraunhofer IGD (D) and the University of Edinburg (UK).

OPALE team is coordinator of the project SOKA, funded by INSEP. The objective is the optimization of the shape of racing canoes in the perspective of 2016 Olympic Games in Rio. Other partners are the Ecole Centrale de Nantes and FFCK (French Federation of Canoe-Kayak).

The OASIS project, Optimization of Addendum Surfaces In Stamping, is an R&D consortium (CS, ArcelorMittal, ErDF, Inria, UTC, EURODECISION, ESILV, NECS, DeltaCAD, SCILAB-DIGITEO) of the Pole Systemtic Paris-Region dedicated to develop an optimal design framework (methods-software platforms-applications) for stamping processes. The EPI OPALE/Inria is the leader within the consortium for the Optimization work-package (one of six WP), the role of which is to develop efficient tools well adapted to Pareto front identification of the multicriteria-dependent stamping processes.

The OASIS project yields 2.4 Meuro total financial support (one Ph.D thesis, two post-doctoral positions and 12 months internship for OPALE).

Type: Cooperation

Defi: Transport (incl. Aeronautics)

Instrument: Coordination and Support Action (CSA)

Objectif: NC

Duration: October 2013 - June 2016

Coordinator: Centre Internacional de Metodes Numerics en Enginyeria, Barcelone (Spain)

Partner: Airbus (Sp), Alenia (I), EADS-IW (F), Rolls-Royce (UK), Ingenia (Sp.), Numeca (B), U. Sheffield (UK), U. Birmingham (UK), CIRA (I), VKI (B), Airbone (NL), Leitat (Sp), Cerfacs (F), U. Cranfield (UK), CAE (CN), GTE (CN), ARI (CN), FAI (CN), ASRI (CN), SAERI (CN), BIAM (CN), ACTRI (CN), BUAA (CN), NPU (CN), PKU (CN), NUAA (CN), ZIU (CN)

Inria contact: Toan Nguyen

Abstract: The main objective of GRAIN2 is to focus its greening activities following the Flight Path 2050 Vision for Aircraft en route to the very ambitious challenge "Protecting the environment and the energy supply" in three major following lines: i) greening the air vehicle, ii) greening the Air transport System and iii) Reducing the carbon foot print of aviation via sustainable alternative fuels. GRAIN2 will identify innovative R & D methods, tools and HPC environments (supercomputers and GPGPUs) in the different KGTs according to the needs of major aeronautical industries to deeper understand the mechanism of engine exhaust emissions, to improve fuel efficiency and environmental performance, to lower noise for landing gear and high lift surfaces, to introduce new materials with multiple functions, to help significantly the development of biofuels for greenhouse gas emission reduction, etc.

Type: FP7

Defi: NC

Instrument: ERC Starting Grant

Objectif: NC

Duration: October 2010 - March 2016

Coordinator: Inria

Inria contact: Paola Goatin

Abstract: The project intends to investigate traffic phenomena from the
macroscopic point of view,
using models derived from fluid-dynamics consisting in hyperbolic
conservation laws.
The scope is to develop a rigorous analytical framework and fast and
efficient numerical tools for solving
optimization and control problems, such as queues lengths control or
buildings exits design.
See also: http://

Type: FP7 (Strep)

Defi: ICT, Technologies for Digital Content and Languages

Instrument: Specific Targeted Research Project

Objectif: Scalable data analytics

Duration: January 2014-December 2016

Coordinator: Centre Internacional de Metodes Numerics en Enginyeria (Spain)

Partners: JOTNE (No.), SINTEF (No.), Fraunhofer IGD (D), ATOS (SP), Univ. Edinburgh (UK)

Inria contact: Toan Nguyen

Abstract: VELaSSCo aims at developing a new concept of integrated end-user visual analysis methods with advanced management and post-processing algorithms for engineering modelling applications, scalable for real-time petabyte level simulations . The interface will enable real-time interrogation of simulation data, generating key information for analysis. Main concerns have to do with handling of large amounts of data of a very specific kind intrinsically linked to geometrical properties; how to store, access, simplify and manipulate billion of records to extract the relevant information; how to represent information in a feasible and flexible way; and how to visualise and interactively inspect the huge quantity of information they produce taking into account end-user's needs. VELaSSCo achieves this by putting together experts with relevant background in Big Data handling, advanced visualisation, engineering simulations, and a User Panel including research centres, SMEs and companies form key European industrial sectors such as aerospace, household products, chemical, pharmaceutical and civil engineering.

Title: Optimal REroute Strategies for Traffic managEment

International Partner (Institution - Laboratory - Researcher):

University of California Berkeley (ÉTATS-UNIS)

Duration: 2012 - 2014

See also: http://

ORESTE is an associated team between OPALE project-team at Inria and the Mobile Millennium / Integrated Corridor Management (ICM) team at UC Berkeley focused on traffic management. With this project, we aim at processing GPS traffic data with up-to-date mathematical techniques to optimize traffic flows in corridors. More precisely, we seek for optimal reroute strategies to reduce freeway congestion employing the unused capacity of the secondary network. The project uses macroscopic traffic flow models and a discrete approach to solve the corresponding optimal control problems. The overall goal is to provide constructive results that can be implemented in practice. Both teams have actively contributed to recent advances in the subject, and we think their collaboration is now mature enough to take advantage of the associate team framework. The Inria team and its theoretical knowledge complement the Berkeley team, with its engineering knowledge anchored in practice.

Inria@SILICONVALLEY :

ORESTE Associated Team with UC Berkeley takes part to the program.

LIRIMA Team ANO 2010-2014:

The agreement governing the creation of the International Laboratory for Research in Computer Science and Applied Mathematics (LIRIMA) was signed on 24th November 2009 in Yaoundé. LIRIMA enables cooperation between Inria research teams and teams in Africa (Sub-Saharan Africa and the Maghreb) to be reinforced. It is the continuation of the major operation undertaken by the SARIMA program (2004-08 Priority Solidarity Fund created by the French Ministry of Foreign & European Affairs).

The LIRIMA team ANO : Numerical analysis of PDEs and Optimization is a partnership between Opale project and the EMI engineering college, Rabat / National Centre for Scientific and Technical Research (CNRST) Morocco. The Team leader is Prof. Rajae Aboulaïch, EMI. Other french participants are the Project Commands at Saclay, Palaiseau and the team-project DRACULA at Inria Lyon.

The ANO team is composed of ten senior researchers from Morocco and ten senior researchers from France and more than fifteen PhD students.

The themes investigated are biomathematics (Models for plants growth, cardiovascular and cerebral diseases, cardio image segmentation), mathematical finance (optimal portfolio, risk management, Islamic finance), multiobjective optimization in structural mechanics, and vehicle traffic and crowd motion. Refer to the website http://www.lirima.uninet.cm/index.php/en/ for more details on the LIRIMA Africa themes and teams.

PHC PROCOPE Team *Transport Networks Modeling and Analysis*

Duration : Jan. 2014- Dec. 2015

Coordinator: P. Goatin (France), S. Göttlich (Germany)

Other partner: University of Mannheim (Germany)

Abstract: The proposed research cooperation focuses on the development and analysis of methods for time-dependent transport phenomena in complex systems. Such systems are given for example by traffic flow networks, production lines, gas and water networks, or chemical reactions. Our particular importance is to model physical processes according to their scale by suitable mathematical means. To this end a model hierarchy using a discrete description for the small scale effects and a continuous model to describe large scale phenomena is investigated. These novel and nonstandard approaches allow to incorporate detailed nonlinear dynamic behavior, which is currently not possible with the widely used classical mixed?integer linear approaches. Through the coupling of discrete and continuous models, both on the theoretical and the applied level, we will contribute to the quantification of uncertainty as well as on control problems for these systems. The modeling is achieved by first considering transport phenomena such as traffic, production, gas and water before controlling the systems. We analyze system properties and derive and implement efficient numerical algorithms for simulation and optimization purposes. In this setting, the proposed project yields a significant contribution for tackling large dynamical problems not only restricted to traffic management but also in other engineering areas.

Pr. Rinaldo M. Colombo

Subject: Conservation laws with non-local flux function.

Institution: Brescia University , Brescia (Italy)

Pr. Simone Göttlich

Subject: Optimization of traffic flows on networks.

Institution: Mannheim University , Mannheim (Germany)

Pr. Moez Kallel

Subject: Data completion for heat-elasticity systems

Institution: ENIT, Tunis al Manar University (Tunisia)

E. Bertino from Ecole Centrale de Nantes (uncertainty quantification in traffic flow models).

C. Fiorini from Politecnico di Milano (multiple gradient descent algorithm applied to unsteady optimization).

S. Scialanga from Roma La Sapienza University (traffic flow models with non-local velocity)

P. Goatin was member of the Scientific Committee of *“CANUM 2014 - 42e Congrès National d'Analyse Numérique"*,
Carry-le-Rouet (France). April 2014. http://

P. Goatin was member of the Scientific Committee of *“PED2014 - Conference on Pedestrian and Evacuation Dynamics"*,
Delft (The Nederlands). October 2014. http://

T. Nguyen is member of the Advisory Board and of the Program Committee of the 9th Intl. Conf. on Networking and Services (ICNS2014). Chamonix (Fr), April 2014. http://

T. Nguyen is member of the Program Committee of the 2nd Intl. Conf. on Smart Grids and Green IT Systems (SMARTGREENS2014). Lisbon (Portugal), May 2014.
http://

T. Nguyen is member of the Program Committee of the 12th Intl. Conf. on Cooperative Design, Visualization and Engineering (CDVE2014), Seatle (USA), September 2014. http://

T. Nguyen is member of the Technical Program Committee and Advisory Chair on Virtualization for the 4th Intl. Conf. on Cloud Computing, GRIDs and Virtualization, Venice (Italy), May 2014.
http://

T. Nguyen is member of the Program Committee of BDCloud2014, Sydney (Australia), December 2014.
http://

R. Duvigneau is reviewers for the following international journals : Comp. & Fluids, Int. J. Num. Fluids, Comp. Meth. Appl. Mech. Eng., Comp. Aided Geom. Design, Appl. Math. & Mech., Eng. Opt.

P. Goatin is reviewers for the following international journals: Acta Applicandæ Mathematicæ ; African Journal of Mathematics and Computer Science Research; Algorithms; Annales de l'Institut Henri Poincaré (C) Analyse Non Linéaire; Applied Mathematics and Computation; Computer-aided Civil and Infrastructure Engineering; Discrete and Continuous Dynamical Systems; European Journal of Operational Research; IEEE Transactions on Automatic Control; IEEE Transactions on Intelligent Transportation Systems; International Journal of Dynamical Systems and Differential Equations; Journal of Computational Physics; Journal of Flow, Turbulence and Combustion; Mathematical Models and Methods in Applied Sciences; Mathematics of Computation; Networks and Heterogeneous Media; New Journal of Physics; Nonlinear Analysis Ser. B: Real World Applications; SIAM Journal of Mathematical Analysis; SIAM Journal of Applied Mathematics; SIAM Journal of Numerical Analysis; SIAM Journal on Scientific Computing.

A. Habbal is reviewer for the following international journals: Applied Mathematics (AM), Scientific Research Publishing Structural and Multidisciplinary Optimization

Master: Advanced Optimization, 40.5 hrs, M2, Ecole Polytechnique Universitaire (EPU), Nice Sophia Antipolis (J.-A. Désidéri, R. Duvigneau).

Master: Conservation laws and finite volume scheme, 30 hrs, M2, Ecole Polytechnique Universitaire (EPU), Nice Sophia Antipolis (P. Goatin).

Master: Multidisciplinary Optimization, 22.5 hrs, joint *Institut Supérieur de l'Aéronautique et de l'Espace*
(ISAE Supaéro, "Complex Systems") and M2 (Mathematics), Toulouse (J.-A. Désidéri, R. Duvigneau).

Licence: Introduction to Numerical Analysis, 19.5hrs, Ecole Polytechnique Universitaire (EPU), Water Engineering, Nice Sophia Antipolis (J.-A. Désidéri).

Licence: Numerical Methods I, 76hrs, Ecole Polytechnique Universitaire (EPU), Nice Sophia Antipolis (A. Habbal).

Licence: Summer Project in Mathematical Modeling, 36 hrs, L3, Ecole Polytechnique Universitaire (EPU), Nice Sophia Antipolis (A. Habbal).

Master: Numerical Methods for Partial Differential Equations, 66 hrs, M1, Ecole Polytechnique Universitaire (EPU), Nice Sophia Antipolis (A. Habbal).

Master: Concurrent design in building structures, M2 Students Project, Ecole Polytechnique Universitaire (EPU), Nice Sophia Antipolis (A. Habbal).

PhD : Maria Laura Delle Monache,
*Traffic flow modeling by conservation laws*,
University of Nice Sophia Antipolis, September 2014.
Supervisor: P. Goatin.

PhD : Jérémie Labroquère,
*Optimization of active devices for turbulent separated flow control*,
University of Nice Sophia Antipolis, November 2014.
Supervisors: Régis Duvigneau and Jean-Antoine Désidéri.

PhD : Maxime Nguyen Dinh,
*Qualification of numerical simulations by anisotropic mesh adaptation*,
University of Nice Sophia Antipolis, March 2014.
Supervisors: Jean-Antoine Désidéri and Jacques Peter (ONERA).

PhD : Enric Roca León,
*Aero-mechanical simulations for rotor blade optimization of helicopter in forward motion*,
University of Nice Sophia Antipolis, October 2014.
Supervisors: Jean-Antoine Désidéri and Arnaud Le Pape (ONERA).

PhD : Aalae Benki,
*Méthodes efficaces de capture de front de Pareto en conception mécanique multicritère. Applications industrielles.*, Jan 2014,
Supervisor: A. Habbal.

PhD : Fatima Zahra Oujebbour,
*Méthodes et applications industrielles en optimisation multicritère de paramètres de processus et de forme en emboutissage*, March 2014,
Supervisor : A. Habbal.

PhD : Mohamed Kaicer,
*Group lending : analysis of asymmetric information using game theory. Analysis design and implementation of a simulator adapted to micro-finance market*, July 2014, Supervisors: R. Aboulaich (Rabat) and A. Habbal.

PhD in progress : Matthias Mimault,
*crowd motion modeling by conservation laws *, Oct. 2012.
Supervisor: P. Goatin.

PhD in progress : Sébastien Bourasseau,
*Méthode de raffinement de maillages non structurés basées sur le vecteur adjoint pour le calcul de coefficients aérodynamiques*,
October 2010. Supervisors: J.-A. Désidéri and J. Peter (ONERA).

PhD in progress : Cédric Durantin,
*Métamodélisation et optimisation pour les dispositifs nanophotoniques*,
October 2014. Supervisors: J.-A. Désidéri and A. Glière (CEA LETI, Grenoble).

PhD in progress : Boutheina Yahyaoui,
*Modélisation de la dynamique des cellules : Des équations de Fisher-KPP aux systèmes mécano-bio-chimiques*, January 2013.
Supervisors: A. Habbal and M. Ayadi (Tunis).

PhD in progress : Maroua Mokni,
*Développement, analyse et évaluation numérique d'un algorithme à multiples gradients (MGDA) pour l'optimisation multicritère*,
April 2014. Supervisors: J.-A. Désidéri and M. Ayadi (Tunis).

PhD in progress : Asma Gdhami,
*Isogeometric methods for hyperbolic systems*,
April 2014. Supervisors: R. Duvigneau and M. Moakher (ENIT).

PhD in progress : Matthieu Sacher,
*Performance Optimization of Racing Yacht Sails*,
October 2014. Supervisors: O. Lemaitre (CNRS LIMSI), R. Duvigneau and F. Hauville (Ecole Navale, Brest).

P. Goatin was member of the selection (Inria Sophia Antipolis) and admission (national) committees for the competitive selection of young graduate scientists (CR2).

P. Goatin authored a short contribution to the book *“Brèves de Maths - Mathématiques de la planète Terre”*, Nouveau Monde ed., 2014.

J.-A. Désidéri is a consultant at ONERA (*The French Aerospace Lab*) and participates in the scientific activities of DSNA
(Department of Numerical Simulation and Aeroacoustics, Châtillon) and DAAP (Department of Applied Aerodynamics, Meudon).