3.1 High order geometric modeling

The accurate description of shapes is a long standing problem in mathematics, with an important impact in many domains, inducing strong interactions between geometry and computation. Developing precise geometric modeling techniques is a critical issue in CAD-CAM. Constructing accurate models, that can be exploited in geometric applications, from digital data produced by cameras, laser scanners, observations or simulations is also a major issue in geometry processing. A main challenge is to construct models that can capture the geometry of complex shapes, using few parameters while being precise.

Our first objective is to develop methods, which are able to describe accurately and in an efficient way, objects or phenomena of geometric nature, using algebraic representations.

The approach followed in CAGD, to describe complex geometry is based on parametric representations called NURBS (Non Uniform Rational B-Spline). The models are constructed by trimming and gluing together high order patches of algebraic surfaces. These models are built from the so-called B-Spline functions that encode a piecewise algebraic function with a prescribed regularity at knots. Although these models have many advantages and have become the standard for designing nowadays CAD models, they also have important drawbacks. Among them, the difficulty to locally refine a NURBS surface and also the topological rigidity of NURBS patches that imposes to use many such patches with trims for designing complex models, with the consequence of the appearing of cracks at the seams. To overcome these difficulties, an active area of research is to look for new blending functions for the representation of CAD models. Some examples are the so-called T-Splines, LR-Spline blending functions, or hierarchical splines, that have been recently devised in order to perform efficiently local refinement. An important problem is to analyze spline spaces associated to general subdivisions, which is of particular interest in higher order Finite Element Methods. Another challenge in geometric modeling is the efficient representation and/or reconstruction of complex objects, and the description of computational domains in numerical simulation. To construct models that can represent efficiently the geometry of complex shapes, we are interested in developing modeling methods, based on alternative constructions such as skeleton-based representations. The change of representation, in particular between parametric and implicit representations, is of particular interest in geometric computations and in its applications in CAGD.

We also plan to investigate adaptive hierarchical techniques, which can locally improve the approximation of a shape or a function. They shall be exploited to transform digital data produced by cameras, laser scanners, observations or simulations into accurate and structured algebraic models.

The precise and efficient representation of shapes also leads to the problem of extracting and exploiting characteristic properties of shapes such as symmetry, which is very frequent in geometry. Reflecting the symmetry of the intended shape in the representation appears as a natural requirement for visual quality, but also as a possible source of sparsity of the representation. Recognizing, encoding and exploiting symmetry requires new paradigms of representation and further algebraic developments. Algebraic foundations for the exploitation of symmetry in the context of non linear differential and polynomial equations are addressed. The intent is to bring this expertise with symmetry to the geometric models and computations developed by aromath.

3.2 Robust algebraic-geometric computation

In many problems, digital data are approximated and cannot just be used as if they were exact. In the context of geometric modeling, polynomial equations appear naturally as a way to describe constraints between the unknown variables of a problem. An important challenge is to take into account the input error in order to develop robust methods for solving these algebraic constraints. Robustness means that a small perturbation of the input should produce a controlled variation of the output, that is forward stability, when the input-output map is regular. In non-regular cases, robustness also means that the output is an exact solution, or the most coherent solution, of a problem with input data in a given neighborhood, that is backward stability.

Our second long term objective is to develop methods to robustly and efficiently solve algebraic problems that occur in geometric modeling.

Robustness is a major issue in geometric modeling and algebraic computation. Classical methods in computer algebra, based on the paradigm of exact computation, cannot be applied directly in this context. They are not designed for stability against input perturbations. New investigations are needed to develop methods which integrate this additional dimension of the problem. Several approaches are investigated to tackle these difficulties.

One relies on linearization of algebraic problems based on “elimination of variables” or projection into a space of smaller dimension. Resultant theory provides a strong foundation for these methods, connecting the geometric properties of the solutions with explicit linear algebra on polynomial vector spaces, for families of polynomial systems (e.g., homogeneous, multi-homogeneous, sparse). Important progress has been made in the last two decades to extend this theory to new families of problems with specific geometric properties. Additional advances have been achieved more recently to exploit the syzygies between the input equations. This approach provides matrix based representations, which are particularly powerful for approximate geometric computation on parametrized curves and surfaces. They are tuned to certain classes of problems and an important issue is to detect and analyze degeneracies and to adapt them to these cases.

A more adaptive approach involves linear algebra computation in a hierarchy of polynomial vector spaces. It produces a description of quotient algebra structures, from which the solutions of polynomial systems can be recovered. This family of methods includes Gröbner Basis , which provides general tools for solving polynomial equations. Border Basis is an alternative approach, offering numerically stable methods for solving polynomial equations with approximate coefficients . An important issue is to understand and control the numerical behavior of these methods as well as their complexity and to exploit the structure of the input system.

In order to compute “only” the (real) solutions of a polynomial system in a given domain, duality techniques can also be employed. They consist in analyzing and adding constraints on the space of linear forms which vanish on the polynomial equations. Combined with semi-definite programming techniques, they provide efficient methods to compute the real solutions of algebraic equations or to solve polynomial optimization problems. The main issues are the completness of the approach, their scalability with the degree and dimension and the certification of bounds.

Singular solutions of polynomial systems can be analyzed by computing differentials, which vanish at these points. This leads to efficient deflation techniques, which transform a singular solution of a given problem into a regular solution of the transformed problem. These local methods need to be combined with more global root localisation methods.

Subdivision methods are another type of methods which are interesting for robust geometric computation. They are based on exclusion tests which certify that no solution exists in a domain and inclusion tests, which certify the uniqueness of a solution in a domain. They have shown their strength in addressing many algebraic problems, such as isolating real roots of polynomial equations or computing the topology of algebraic curves and surfaces. The main issues in these approaches is to deal with singularities and degenerate solutions.

4.1 Geometric modeling for Design and Manufacturing.

The main domain of applications that we consider for the methods we develop is Computer Aided Design and Manufacturing.

Computer-Aided Design (CAD) involves creating digital models defined by mathematical constructions, from geometric, functional or aesthetic considerations. Computer-aided manufacturing (CAM) uses the geometrical design data to control the tools and processes, which lead to the production of real objects from their numerical descriptions.

CAD-CAM systems provide tools for visualizing, understanding, manipulating, and editing virtual shapes. They are extensively used in many applications, including automotive, shipbuilding, aerospace industries, industrial and architectural design, prosthetics, and many more. They are also widely used to produce computer animation for special effects in movies, advertising and technical manuals, or for digital content creation. Their economic importance is enormous. Their importance in education is also growing, as they are more and more used in schools and educational purposes.

CAD-CAM has been a major driving force for research developments in geometric modeling, which leads to very large software, produced and sold by big companies, capable of assisting engineers in all the steps from design to manufacturing.

Nevertheless, many challenges still need to be addressed. Many problems remain open, related to the use of efficient shape representations, of geometric models specific to some application domains, such as in architecture, naval engineering, mechanical constructions, manufacturing ...Important questions on the robustness and the certification of geometric computation are not yet answered. The complexity of the models which are used nowadays also appeals for the development of new approaches. The manufacturing environment is also increasingly complex, with new type of machine tools including: turning, 5-axes machining and wire EDM (Electrical Discharge Machining), 3D printer. It cannot be properly used without computer assistance, which raises methodological and algorithmic questions. There is an increasing need to combine design and simulation, for analyzing the physical behavior of a model and for optimal design.

The field has deeply changed over the last decades, with the emergence of new geometric modeling tools built on dedicated packages, which are mixing different scientific areas to address specific applications. It is providing new opportunities to apply new geometric modeling methods, output from research activities.

4.2 Geometric modeling for Numerical Simulation and Optimization

A major bottleneck in the CAD-CAM developments is the lack of interoperability of modeling systems and simulation systems. This is strongly influenced by their development history, as they have been following different paths.

The geometric tools have evolved from supporting a limited number of tasks at separate stages in product development and manufacturing, to being essential in all phases from initial design through manufacturing.

Current Finite Element Analysis (FEA) technology was already well established 40 years ago, when CAD-systems just started to appear, and its success stems from using approximations of both the geometry and the analysis model with low order finite elements (most often of degree $\le 2$).

There has been no requirement between CAD and numerical simulation, based on Finite Element Analysis, leading to incompatible mathematical representations in CAD and FEA. This incompatibility makes interoperability of CAD/CAM and FEA very challenging. In the general case today this challenge is addressed by expensive and time-consuming human intervention and software developments.

Improving this interaction by using adequate geometric and functional descriptions should boost the interaction between numerical analysis and geometric modeling, with important implications in shape optimization. In particular, it could provide a better feedback of numerical simulations on the geometric model in a design optimization loop, which incorporates iterative analysis steps.

The situation is evolving. In the past decade, a new paradigm has emerged to replace the traditional Finite Elements by B-Spline basis element of any polynomial degree, thus in principle enabling exact representation of all shapes that can be modeled in CAD. It has been demonstrated that the so-called isogeometric analysis approach can be far more accurate than traditional FEA.

It opens new perspectives for the interoperability between geometric modeling and numerical simulation. The development of numerical methods of high order using a precise description of the shapes raises questions on piecewise polynomial elements, on the description of computational domains and of their interfaces, on the construction of good function spaces to approximate physical solutions. All these problems involve geometric considerations and are closely related to the theory of splines and to the geometric methods we are investigating. We plan to apply our work to the development of new interactions between geometric modeling and numerical solvers.

6.1 New software

7.1 Computing real radicals by moment optimization

Participants: Lorenzo Baldi, Bernard Mourrain.

7.2 Multilinear Polynomial Systems: Root Isolation and Bit Complexity

Participants: Ioannis Emiris, Angelos Mantzaflaris, Elias Tsigaridas.

In 17 we exploit structure in polynomial system solving by considering polynomials that are linear in subsets of the variables. We focus on algorithms and their Boolean complexity for computing isolating hyperboxes for all the isolated complex roots of well-constrained, unmixed systems of multilinear polynomials based on resultant methods. We enumerate all expressions of the multihomogeneous (or multigraded) resultant of such systems as a determinant of Sylvester-like matrices, aka generalized Sylvester matrices. We construct these matrices by means of Weyman homological complexes, which generalize the Cayley-Koszul complex. The computation of the determinant of the resultant matrix is the bottleneck for the overall complexity. We exploit the quasi-Toeplitz structure to reduce the problem to efficient matrix-vector multiplication, which corresponds to multivariate polynomial multiplication, by extending the seminal work on Macaulay matrices of Canny, Kaltofen, and Yagati to the multi-homogeneous case. We compute a rational univariate representation of the roots, based on the primitive element method. We present an algorithmic variant to compute the isolated roots of overdetermined and positive-dimensional systems. Thus our algorithms and complexity analysis apply in general with no assumptions on the input.

7.3 Multivariate Interpolation: Preserving and Exploiting Symmetry

Participant: Evelyne Hubert, Erick David Rodriguez-Bazan.

Interpolation is a prime tool in algebraic computation while symmetry is a qualitative feature that can be more relevant to a mathematical model than the numerical accuracy of the parameters. The article 21 shows how to exactly preserve symmetry in multivariate interpolation while exploiting it to alleviate the computational cost. We revisit minimal degree and least interpolation with symmetry adapted bases, rather than monomial bases. For a space of linear forms invariant under a group action, we construct bases of invariant interpolation spaces in blocks, capturing the inherent redundancy in the computations. With the so constructed symmetry adapted interpolation bases, the uniquely defined interpolant automatically preserves any equivariance the interpolation problem might have. Even with no equivariance, the computational cost to obtain the interpolant is alleviated thanks to the smaller size of the matrices to be inverted.

7.4 Sparse Interpolation in Terms of Multivariate Chebyshev Polynomials

Participant: Evelyne Hubert.

Sparse interpolation refers to the exact recovery of a function as a short linear combination of basis functions from a limited number of evaluations. For multivariate functions, the case of the monomial basis is well studied, as is now the basis of exponential functions. Beyond the multivariate Chebyshev polynomial obtained as tensor products of univariate Chebyshev polynomials, the theory of root systems allows to define a variety of generalized multivariate Chebyshev polynomials that have connections to topics such as Fourier analysis and representations of Lie algebras. The article 18 presents a deterministic algorithm to recover a function that is the linear combination of at most r such polynomials from the knowledge of $r$ and an explicitly bounded number of evaluations of this function.

This is joint work with Michael Singer, North Carolina State University, USA.

7.5 Euclidean embeddings of distance graphs

Participants: Evangelos Bartzos, Ioannis Emiris, Charalampos Tzamos.

We employ the m-Bézout Bound for Distance Geometry problems, namely for estimating the number of embeddings of a simple weighted graph 26. We use combinatorial methods based on our previous work that gave the first nontrivial bounds for this problem, thus offering the first improvement upon Bezout's bound after a few decades of research in this area. In this paper, we moreover examine the opposite direction of using combinatorial bounds to estimate the number of roots of a multihomogenous well-constrained system when the latter has some extra structure.

This joint work is based on I. Emiris' invited plenary talk at CASC 2021.

7.6 Koszul-type determinantal formulas for families of mixed multilinear systems

Participants: Angelos Mantzaflaris.

Effective computation of resultants is a central problem in elimination theory and polynomial system solving. Commonly, we compute the resultant as a quotient of determinants of matrices and we say that there exists a determinantal formula when we can express it as a determinant of a matrix whose elements are the coefficients of the input polynomials. In 12, we study the resultant in the context of mixed multilinear polynomial systems, that is multilinear systems with polynomials having different supports, on which determinantal formulas were not known. We construct determinantal formulas for two kind of multilinear systems related to the Multiparameter Eigenvalue Problem (MEP): first, when the polynomials agree in all but one block of variables; second, when the polynomials are bilinear with different supports, related to a bipartite graph. We use the Weyman complex to construct Koszul-type determinantal formulas that generalize Sylvester-type formulas. We can use the matrices associated to these formulas to solve square systems without computing the resultant. The combination of the resultant matrices with the eigenvalue and eigenvector criterion for polynomial systems leads to a new approach for solving MEP.

7.7 Determinantal tensor product surfaces and the method of moving quadrics

Participants: Laurent Busé.

A tensor product surface $𝒮$ is an algebraic surface that is defined as the closure of the image of a rational map $\varphi$ from ${\mathbb{P}}^1\times {\mathbb{P}}^1$ to ${\mathbb{P}}^3$. In 13, we provide new determinantal representations of $𝒮$ under the assumptions that $\varphi$ is generically injective and its base points are finitely many and locally complete intersections. These determinantal representations are matrices that are built from the coefficients of linear relations (syzygies) and quadratic relations of the bihomogeneous polynomials defining $\varphi$. Our approach relies on a formalization and generalization of the method of moving quadrics introduced and studied by David Cox and his co-authors.

This is joint work with Falai Chen (University of Science and Technology of China at Hefei).

7.8 The Hessian polynomial and the Jacobian ideal of a reduced hypersurface in ${\mathbb{P}}^n$

Participants: Laurent Busé.

For a reduced hypersurface $V\left(f\right)\subseteq {\mathbb{P}}^n$ of degree $d$, the Castelnuovo-Mumford regularity of the Milnor algebra $M\left(f\right)$ is well understood when $V\left(f\right)$ is smooth, as well as when $V\left(f\right)$ has isolated singularities. In 14, we study the regularity of $M\left(f\right)$ when $V\left(f\right)$ has a positive dimensional singular locus. In certain situations, we prove that the regularity is bounded by $(d-2)(n+1)$, which is the degree of the Hessian polynomial of $f$. However, this is not always the case, and we prove that in ${\mathbb{P}}^n$ the regularity of the Milnor algebra can grow quadratically in $d$.

This is joint work with Alexandru Dimca (UCA), Hal Schenck (Department of Mathematics and Statistics at Auburn University) and Gabriel Sticlaru (Faculty of Mathematics and Informatics at Constanta).

7.9 Progressive Discrete Domains for Implicit Surface Reconstruction

Participants: Laurent Busé.

Many global implicit surface reconstruction algorithms formulate the problem as a volumetric energy minimization, trading data fitting for geometric regularization. As a result, the output surfaces may be located arbitrarily far away from the input samples. This is amplified when considering i) strong regularization terms, ii) sparsely distributed samples or iii) missing data. This breaks the strong assumption commonly used by popular octree-based and triangulation-based approaches that the output surface should be located near the input samples. As these approaches refine during a pre-process, their cells near the input samples, the implicit solver deals with a domain discretization not fully adapted to the final isosurface. In 24, we relax this assumption and propose a progressive coarse-to-fine approach that jointly refines the implicit function and its representation domain, through iterating solver, optimization and refinement steps applied to a 3D Delaunay triangulation. There are several advantages to this approach: the discretized domain is adapted near the isosurface and optimized to improve both the solver conditioning and the quality of the output surface mesh contoured via marching tetrahedra.

This is joint work with Tong Zhao (Titane), Pierre Alliez (Titane), Tamy Boubekeur (LTCI) and Jean-Marc Thiery (LTCI).

7.10 Delaunay Meshing and Repairing of NURBS Models

Participants: Laurent Busé.

CAD models represented by NURBS surface patches are often hampered with defects due to inaccurate representations of trimming curves. Such defects make these models unsuitable to the direct generation of valid volume meshes, and often require trial-and-error processes to fix them. In 23, we propose a fully automated Delaunay-based meshing approach which can mesh and repair simultaneously, while being independent of the input NURBS patch layout. Our approach proceeds by Delaunay filtering and refinement, in which trimmed areas are repaired through implicit surfaces. Beyond repair, we demonstrate its capability to smooth out sharp features, defeature small details, and mesh multiple domains in contact.

This is joint work with Xiao Xiao (Titane), Pierre Alliez (Titane) and Laurent Rineau (GeometryFactory).

7.11 Computing the Topology of Voronoi Diagrams of Parallel Half-Lines

Participant: Bernard Mourrain.

This is a joint work with Ibrahim Adamou, Departement de Mathématiques, Faculté des Sciences et Techniques, Université Dan Dicko Dankoulodo de Maradi.

7.12 Riemannian Newton optimization methods for the symmetric tensor approximation problem

Participants: Rima Khouja, Bernard Mourrain.

This is a joint work with Houssam Khalil, Faculty of Sciences, Lebanese University.

7.13 Scientific computing for portfolio optimization

Participants: Ioannis Emiris, Apostolos Chalkis, Emmanouil Christoforou, Theodore Dalamagas.

In 29 we develop geometric methods for modeling asset allocations. Portfolios correspond to points in a simplex whose dimension equals the number of assets or stocks, assuming the total invested quantity is fixed. This allows us to model Crisis Periods in Stock Markets by computing the tradeoff between expected returns and the volatility (variance) of returns by means of copulas. The computation of copulas uses the aforementioned geometric tools. In 16 we go one step forward in portfolio management: Estimating the probability distribution for achieving certain returns leads us to a new portfolio performance score which reflects the probability of sufficiently high profit.

7.14 Machine learning for wind speed forecasting

Participants: Emmanouil Christoforou, Ioannis Emiris, Apostolos Florakis, Despina Rizou, Stella Zaharia.

In 15 we develop neural networks adapted to the problem of wind speed forecasting. The input are classical meteorological predictions, and the training is undertaken over more than 12 months in specific locations in Greece, in collaboration with a wind power producer company. We introduce a novel architecture that combines CNN for capturing the location's landscape and Recurrent NN (RNN) since wind speed is a time-dependent phenomenon. Our results improve the published state-of-the-art for 24-hour predictions. Note that our model does rely on previous day information as most models do today.

7.15 Routing optimization

Participants: Ioannis Emiris.

In 28 we employ 2D kinetic Voronoi diagrams for Autonomous and semi-autonomous ship routing in ports, straits and other narrow domains. This relies on CGAL packages for such operations. Second, we propose capacitated vehicle routing algorithms with time-windows for optimized ship routing: these methods have been developed for land routing when vehicles have several capacity and loading constraints, and we expect that they may be similarly useful in shipping over a graph representing the various ports to be visited, including one or more depots or loading ports.

7.16 Spirometry-based airways disease simulation and recognition using Machine Learning approaches

Participants: Riccardo DiDio, André Galligo, Angelos Mantzaflaris.

In 27 we pursue the purpose of providing means to physicians for automated and fast recognition of airways diseases. In this work, we mainly focus on measures that can be easily recorded using a spirometer. The signals used in this framework are simulated using the linear bi-compartment model of the lungs. This allows us to simulate ventilation under the hypothesis of ventilation at rest (tidal breathing). By changing the resistive and elastic parameters, data samples are realized simulating healthy, fibrosis and asthma breathing. On this synthetic data, different machine learning models are tested and their performance is assessed. All but the Naive bias classifier show accuracy of at least 99%. This represents a proof of concept that Machine Learning can accurately differentiate diseases based on manufactured spirometry data. This paves the way for further developments on the topic, notably testing the model on real data.

7.17 Stretch-Based Hyperelastic Material Formulations for Isogeometric Kirchhoff-Love Shells with Application to Wrinkling

Participants: Angelos Mantzaflaris.

Modelling nonlinear phenomena in thin rubber shells calls for stretch-based material models, such as the Ogden model which conveniently utilizes eigenvalues of the deformation tensor. Derivation and implementation of such models have been already made in Finite Element Methods. This is, however, still lacking in shell formulations based on Isogeometric Analysis, where higher-order continuity of the spline basis is employed for improved accuracy. The article 22 fills this gap by presenting formulations of stretch-based material models for isogeometric Kirchhoff-Love shells. We derive general formulations based on explicit treatment in terms of derivatives of the strain energy density functions with respect to principal stretches for (in)compressible material models where determination of eigenvalues as well as the spectral basis transformations is required. Using several numerical benchmarks, we verify our formulations on invariant-based Neo-Hookean and Mooney-Rivlin models and with a stretch-based Ogden model. In addition, the model is applied to simulate collapsing behaviour of a truncated cone and it is used to simulate tension wrinkling of a thin sheet.

7.18 Practical isogeometric shape optimization: Parameterization by means of regularization

Participants: Angelos Mantzaflaris.

Shape optimization based on Isogeometric Analysis (IGA) has gained popularity in recent years. Performing shape optimization directly over parameters defining the CAD geometry, such as for example the control points of a spline parametrization, opens up the prospect of seamless integration of a shape optimization step into the CAD workflow. One of the challenges when using IGA for shape optimization is that of maintaining a valid geometry parametrization of the interior of the domain during an optimization process, as the shape of the boundary is altered by an optimization algorithm. Existing methods impose constraints on the Jacobian of the parametrization, to guarantee that the parametrization remains valid. The number of such validity constraints quickly becomes intractably large, especially when 3D shape optimization problems are considered. An alternative, and arguably simpler, approach is to formulate the isogeometric shape optimization problem in terms of both the boundary and the interior control points. In order to ensure a geometric parametrization of sufficient quality a regularization term, such as the Winslow functional, is added to the objective function of the shape optimization problem. In 20 we illustrate the performance of these methods on the optimal design problem of electromagnetic reflectors and compare their performance. Both methods are implemented for multipatch geometries, using the IGA library G+Smo and the optimization library Ipopt. We find that the second approach performs comparably to a state of the art method with respect to both the quality of the found solutions and computational time, while its performance in our experience is more robust for coarse discretizations.

8.1 Bilateral contracts with industry

$•$ NURBSFIX: Repairing the topology of a NURBS model in view of its approximation.

Participants: Laurent Busé.

$•$ Geometric computing.

Participants: Ioannis Emiris, Apostolos Chalkis, Panagiotis Repouskos, Thomas Pappas, Ioannis Psarros.

Ioannis Emiris coordinates a research contract with the industrial partner ANSYS (Greece), in collaboration with Athena Research Center. MSc students P. Repouskos and T. Pappas, PhD candidate A. Chalkis and postdoc fellow I. Psarros are partially funded.

Electronic design automation (EDA) and simulating Integrated Circuits requires robust geometric operations on thousands of electronic elements (capacitors, resistors, coils etc) represented by polyhedral objects in 2.5 dimensions, not necessarily convex. A special case may concern axis-aligned objects but the real challenge is the general case. The project, extended into 2021, focuses on 3 axes: (1) efficient data structures and prototype implementations for storing the aforementioned polyhedral objects so that nearest neighbor queries are fast in the L-max metric, which is the primary focus of the contract, (2) random sampling of the free space among objects, (3) data-driven algorithmic design for problems concerning data-structures and their construction and initialization.

8.2 Bilateral grants with industry

$•$ Interactive construction of 3D models - Application to the modeling of complex geological structures.

Participants: Ayoub Belhachmi, Bernard Mourrain.

CIFRE collaboration between Schlumberger Montpellier (A. Azzedine) and Inria Sophia Antipolis (B. Mourrain). The PhD candidate is A. Belhachmi. The objective of the work is the development of a new spline based high quality geomodeler for reconstructing the stratigraphy of geological layers from the adaptive and efficient processing of large terrain information.

9.1 International initiatives

9.2 European initiatives

9.3 National initiatives

GdR EFI and GDM: Evelyne Hubert is part of the Scientific Committee of the GdR Equations Fonctionnelles et Interactions (gdrefi.math.cnrs.fr) and participates to the GdR Géometrie Differentielle et Mécanique (gdr-gdm.univ-lr.fr).

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

11.1 Major publications

• 1 articleE.Evangelos Bartzos, I. Z.Ioannis Z. Emiris and J.Josef Schicho. On the multihomogeneous Bézout bound on the number of embeddings of minimally rigid graphs.Applicable Algebra in Engineering, Communication and Computing315-62020, 325-357
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• 2 articleL.Laurent Busé, Y.Yairon Cid-Ruiz and C.Carlos D'Andrea. Degree and birationality of multi-graded rational maps.Proceedings of the London Mathematical Society12142020, 743-787
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• 3 articleL.Laurent Busé and A.Anna Karasoulou. Resultant of an equivariant polynomial system with respect to the symmetric group.Journal of Symbolic Computation762016, 142-157
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• 4 articleI. Z.Ioannis Z. Emiris and I.Ioannis Psarros. Products of Euclidean Metrics, Applied to Proximity Problems among Curves.ACM Transactions on Spatial Algorithms and Systems64August 2020, 1-20
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• 5 articleA. J.Alvaro Javier Fuentes Suárez and E.Evelyne Hubert. Scaffolding skeletons using spherical Voronoi diagrams: feasibility, regularity and symmetry.Computer-Aided Design102May 2018, 83 - 93
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• 6 articleA.Alessandro Giust, B.Bert Jüttler and A.Angelos Mantzaflaris. Local (T)HB-spline projectors via restricted hierarchical spline fitting.Computer Aided Geometric Design80June 2020, 101865
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• 7 articleP.Paul Görlach, E.Evelyne Hubert and T.Théo Papadopoulo. Rational invariants of even ternary forms under the orthogonal group.Foundations of Computational Mathematics192019, 1315-1361
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• 8 articleZ.Zbigniew Jelonek and A.André Galligo. Elimination ideals and Bezout relations.Journal of Algebra5622020, 621-626
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• 9 articleA.Angelos Mantzaflaris, B.Bert Jüttler, B.Boris Khoromskij and U.Ulrich Langer. Low Rank Tensor Methods in Galerkin-based Isogeometric Analysis.Computer Methods in Applied Mechanics and Engineering316April 2017, 1062-1085
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• 10 articleB.Bernard Mourrain. Polynomial-Exponential Decomposition from Moments.Foundations of Computational Mathematics186December 2018, 1435--1492
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• 11 articleS.Simon Telen, B.Bernard Mourrain and M.Marc Van Barel. Solving Polynomial Systems via a Stabilized Representation of Quotient Algebras.SIAM Journal on Matrix Analysis and Applications393October 2018, 1421--1447
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11.2 Publications of the year

11.3 Other