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Tangent Vector Kernels for Invariant Image Classification with SVMs

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Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Fabio Nobile, Eleonora Musharbash

In this paper we propose a dynamical low-rank strategy for the approximation of second order wave equations with random parameters. The governing equation is rewritten in Hamiltonian form and the approximate solution is expanded over a set of

2S

dynamica ...

2017

Tangent Vector Kernels for Invariant Image Classification with SVMs

Graph Chatbot

Chattez avec Graph Search

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Rank-adaptive structure-preserving model order reduction of Hamiltonian systems

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Structure-preserving approaches and data-driven closure modeling for model order reduction

Symplectic dynamical low rank approximation of wave equations with random parameters

Dictionary Learning for Two-Dimensional Kendall Shapes

Dynamical Reduced Basis Methods for Hamiltonian Systems

Procrustes Metrics and Optimal Transport for Covariance Operators

Distinct distances between points and lines

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Procrustes Metrics and Optimal Transport for Covariance Operators

Dynamical Reduced Basis Methods for Hamiltonian Systems

Dictionary Learning for Two-Dimensional Kendall Shapes

Symplectic dynamical low rank approximation of wave equations with random parameters

Structure-preserving approaches and data-driven closure modeling for model order reduction

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Rank-adaptive structure-preserving model order reduction of Hamiltonian systems

Distinct distances between points and lines