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MATHICSE Technical Report: Reduced basis methods: from low-rank matrices to low-rank tensor

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MATHICSE Technical Report: Reduced basis methods: from low-rank matrices to low-rank tensor

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Chattez avec Graph Search

A mathematical theory for mass lumping and its generalization with applications to isogeometric analysis

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Robust Controller Design For Linear Systems With Nonlinear Distortions: A Data-Driven Approach

Robust Controller Design For Linear Systems With Nonlinear Distortions: A Data-Driven Approach

On infinite dimensional linear programming approach to stochastic control * *This research is partially supported by M. Kamgarpour’s European Union ERC Starting Grant, CONENE and by T. Summers’ the US National Science Foundation under grant CNS-1566127.

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Finite strain FFT-based non-linear solvers made simple

Scalable Low-rank Matrix and Tensor Decomposition on Graphs

Reduced Basis Methods: From Low-Rank Matrices To Low-Rank Tensors

On the preconditioning of the INTERNODES matrix for applications in contact mechanics

Finite strain FFT-based non-linear solvers made simple

Scalable Low-rank Matrix and Tensor Decomposition on Graphs

Reduced Basis Methods: From Low-Rank Matrices To Low-Rank Tensors

Robust Controller Design For Linear Systems With Nonlinear Distortions: A Data-Driven Approach

Robust Controller Design For Linear Systems With Nonlinear Distortions: A Data-Driven Approach

A mathematical theory for mass lumping and its generalization with applications to isogeometric analysis

Parameter Determination of Sensor Stochastic Models under Covariate Dependency

Unlabeled Sensing with Random Linear Measurements

On infinite dimensional linear programming approach to stochastic control * *This research is partially supported by M. Kamgarpour’s European Union ERC Starting Grant, CONENE and by T. Summers’ the US National Science Foundation under grant CNS-1566127.