Publication date
March 2021
DOIAbstract
This software contains code supporting the numerical experiments for the preprint A Training Set Subsampling Strategy for the Reduced Basis Method, authored by Sridhar Chellappa, Lihong Feng and Peter Benner
This software contains code supporting the numerical experiments for the preprint A Training Set Subsampling Strategy for the Reduced Basis Method, authored by Sridhar Chellappa, Lihong Feng and Peter Benner
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In recent years many advances have been made in solution techniques for specially structured 0–1 int...
Final version, to appear in Stochastic SystemsInternational audienceWe derive a stochastic gradient ...
Software for A Relaxed Localized Trust-Region Reduced Basis Approach for Optimization of Multiscale ...
Abstract: The reduced basis (RB) method is an efficient technique to solve parametric partial differ...
International audienceThe Reduced Basis Method Motivations • Modeling : multi-physics non-linear mod...
This is the supplementary Software for the PhD thesis "Adaptive Reduced Basis Methods for Multiscal...
Source code to reproduce the results in the publication "Exponential Convergence of Online Enrichmen...
Abstract. The reduced basis method is a model order reduction method for parametrized partial differ...
The Reduced Basis Method (RBM) has become a widespread model reduction technique for parametrized pa...
Abstract. Numerical approximation of the solution of partial differential equations plays an importa...
This data contains the code and images necessary to reproduce the computational results published in...
Code base for paper "Reinforcement learning prioritizes general applicability in reaction optimizati...
Subsampling algorithms are a natural approach to reduce data size before fitting models on massive d...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In recent years many advances have been made in solution techniques for specially structured 0–1 int...
Final version, to appear in Stochastic SystemsInternational audienceWe derive a stochastic gradient ...
Software for A Relaxed Localized Trust-Region Reduced Basis Approach for Optimization of Multiscale ...
Abstract: The reduced basis (RB) method is an efficient technique to solve parametric partial differ...
International audienceThe Reduced Basis Method Motivations • Modeling : multi-physics non-linear mod...
This is the supplementary Software for the PhD thesis "Adaptive Reduced Basis Methods for Multiscal...
Source code to reproduce the results in the publication "Exponential Convergence of Online Enrichmen...
Abstract. The reduced basis method is a model order reduction method for parametrized partial differ...
The Reduced Basis Method (RBM) has become a widespread model reduction technique for parametrized pa...
Abstract. Numerical approximation of the solution of partial differential equations plays an importa...
This data contains the code and images necessary to reproduce the computational results published in...
Code base for paper "Reinforcement learning prioritizes general applicability in reaction optimizati...
Subsampling algorithms are a natural approach to reduce data size before fitting models on massive d...
Various regularization techniques are investigated in supervised learning from data. Theoretical fea...
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
In recent years many advances have been made in solution techniques for specially structured 0–1 int...
Final version, to appear in Stochastic SystemsInternational audienceWe derive a stochastic gradient ...
Add to ORKG