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## Multi angle  Multiscale model reduction for flows in heterogeneous porous media Calo, Victor (Auteur de la Conférence) | CIRM (Editeur )

We combine discrete empirical interpolation techniques, global mode decomposition methods, and local multiscale methods, such as the Generalized Multiscale Finite Element Method (GMsFEM), to reduce the computational complexity associated with nonlinear flows in highly-heterogeneous porous media. To solve the nonlinear governing equations, we employ the GMsFEM to represent the solution on a coarse grid with multiscale basis functions and apply proper orthogonal decomposition on a coarse grid. Computing the GMsFEM solution involves calculating the residual and the Jacobian on the fine grid. As such, we use local and global empirical interpolation concepts to circumvent performing these computations on the fine grid. The resulting reduced-order approach enables a significant reduction in the flow problem size while accurately capturing the behavior of fully-resolved solutions. We consider several numerical examples of nonlinear multiscale partial differential equations that are numerically integrated using fully-implicit time marching schemes to demonstrate the capability of the proposed model reduction approach to speed up simulations of nonlinear flows in high-contrast porous media.

Keywords: generalized multiscale finite element method - nonlinear PDEs - heterogeneous porous media - discrete empirical interpolation - proper orthogonal decomposition
We combine discrete empirical interpolation techniques, global mode decomposition methods, and local multiscale methods, such as the Generalized Multiscale Finite Element Method (GMsFEM), to reduce the computational complexity associated with nonlinear flows in highly-heterogeneous porous media. To solve the nonlinear governing equations, we employ the GMsFEM to represent the solution on a coarse grid with multiscale basis functions and apply ...

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## Multi angle  On hybrid method for rariefied gas dynamics : Boltzmann vs. Navier-Stokes models Filbet, Francis (Auteur de la Conférence) | CIRM (Editeur )

We construct a hierarchy of hybrid numerical methods for multi-scale kinetic equations based on moment realizability matrices, a concept introduced by Levermore, Morokoff and Nadiga. Following such a criterion, one can consider hybrid scheme where the hydrodynamic part is given either by the compressible Euler or Navier-Stokes equations, or even with more general models, such as the Burnett or super-Burnett systems.
PDE - numerical methods - Boltzmann equation - fluid models - hybrid methods
We construct a hierarchy of hybrid numerical methods for multi-scale kinetic equations based on moment realizability matrices, a concept introduced by Levermore, Morokoff and Nadiga. Following such a criterion, one can consider hybrid scheme where the hydrodynamic part is given either by the compressible Euler or Navier-Stokes equations, or even with more general models, such as the Burnett or super-Burnett systems.
PDE - numerical methods - ...

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## Multi angle  Numerical studies of space filling designs: optimization algorithm and subprojection properties Iooss, Bertrand (Auteur de la Conférence) | CIRM (Editeur )

discrepancy, optimal design, Latin Hypercube Sampling, computer experiment

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## Multi angle  Discontinuous Galerkin solver design on hybrid computers Helluy, Philippe (Auteur de la Conférence) | CIRM (Editeur )

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## Multi angle  Simulation of kinetik electrostatic electron nonlinear (KEEN) waves with variable velocity resolution grids and high-order time-splitting Mehrenberger, Michel (Auteur de la Conférence) | CIRM (Editeur )

KEEN - Vlasov plasmas - acoustic waves - semi-Lagrangian scheme - Vlasov-Poisson equation; - BGK mode

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## Multi angle  Arbitrages in a progressive enlargement of filtration Jeanblanc, Monique (Auteur de la Conférence) | CIRM (Editeur )

We study a financial market in which some assets, with prices adapted w.r.t. a reference filtration F are traded. In this presentation, we shall restrict our attention to the case where F is generated by a Brownian motion. One then assumes that an agent has some extra information, and may use strategies adapted to a larger filtration G. This extra information is modeled by the knowledge of some random time $\tau$, when this time occurs. We restrict our study to a progressive enlargement setting, and we pay particular attention to honest times. Our goal is to detect if the knowledge of $\tau$ allows for some arbitrage (classical arbitrages and arbitrages of the first kind), i.e., if using G-adapted strategies, one can make profit. The results presented here are based on two joint papers with Aksamit, Choulli and Deng, in which the authors study No Unbounded Profit with Bounded Risk (NUPBR) in a general filtration F and the case of classical arbitrages in the case of honest times, density framework and immersion setting. We shall also study the information drift and the growth of an optimal portfolio resulting from that model (forthcoming work with T. Schmidt). We study a financial market in which some assets, with prices adapted w.r.t. a reference filtration F are traded. In this presentation, we shall restrict our attention to the case where F is generated by a Brownian motion. One then assumes that an agent has some extra information, and may use strategies adapted to a larger filtration G. This extra information is modeled by the knowledge of some random time $\tau$, when this time occurs. We ...

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## Multi angle  Adaptive low-rank approximations for stochastic and parametric equations: a subspace point of view Nouy, Anthony (Auteur de la Conférence) | CIRM (Editeur )

Tensor methods have emerged as an indispensable tool for the numerical solution of high-dimensional problems in computational science, and in particular problems arising in stochastic and parametric analyses. In many practical situations, the approximation of functions of multiple parameters (or random variables) is made computationally tractable by using low-rank tensor formats. Here, we present some results on rank-structured approximations and we discuss the connection between best approximation problems in tree-based low-rank formats and the problem of finding optimal low-dimensional subspaces for the projection of a tensor. Then, we present constructive algorithms that adopt a subspace point of view for the computation of sub-optimal low-rank approximations with respect to a given norm. These algorithms are based on the construction of sequences of suboptimal but nested subspaces.

Keywords: high dimensional problems - tensor numerical methods - projection-based model order reduction - low-rank tensor formats - greedy algorithms - proper generalized decomposition - uncertainty quantification - parametric equations
Tensor methods have emerged as an indispensable tool for the numerical solution of high-dimensional problems in computational science, and in particular problems arising in stochastic and parametric analyses. In many practical situations, the approximation of functions of multiple parameters (or random variables) is made computationally tractable by using low-rank tensor formats. Here, we present some results on rank-structured approximations ...