FAUDCNAvHSeminar

1 to 6 of 66

08JUL 2022
2022-resources
Quantitative rapid stabilization of some PDE models
Date: Fri. July 08, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) Title: Quantitative rapid stabilization of some PDE models Speaker: Prof. Dr. Shengquan Xiang Affiliation: Mathematical Institute for Data Science at Johns Hopkins University (USA) Abstract. Quantitative stabilization is an active […]Date: Fri. July 08, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany) Title: Quantitative rapid stabilization of some PDE models Speaker: Prof. Dr. Shengquan Xiang Affiliation: Mathematical Institute for Data Science at Johns Hopkins University (USA) Abstract. Quantitative stabilization is an active […]
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05JUL 2022
2022-resources
Quasi-reversibility methods of optimal control for ill-posed final value diffusion equations
Date: Tue. July 05, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship Title: Quasi-reversibility methods of optimal control for ill-posed final value diffusion equations Speaker: Visitor scientist Prof. Dr. Mahamadi Warma Affiliation: George Mason University (USA) Abstract. We consider optimal control problems associated to generally non-well posed […]Date: Tue. July 05, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship Title: Quasi-reversibility methods of optimal control for ill-posed final value diffusion equations Speaker: Visitor scientist Prof. Dr. Mahamadi Warma Affiliation: George Mason University (USA) Abstract. We consider optimal control problems associated to generally non-well posed […]
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22JUN 2022
2022-resources
Physics-inspired equivariant machine learning
Date: Wed. June 22, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Erlangen-Nürnberg (Germany) Title: Physics-inspired equivariant machine learning Speaker: Prof. Dr. Soledad Villar Affiliation: Mathematical Institute for Data Science at Johns Hopkins University (USA) Abstract. There has been enormous progress in the last few […]Date: Wed. June 22, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Erlangen-Nürnberg (Germany) Title: Physics-inspired equivariant machine learning Speaker: Prof. Dr. Soledad Villar Affiliation: Mathematical Institute for Data Science at Johns Hopkins University (USA) Abstract. There has been enormous progress in the last few […]
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10JUN 2022
2022-resources
Deep redatuming for PDE and inverse problems
Date: Fri. June 10, 2022 Organized by: FAU DCN-AvH, Chair for Dynamics, Control and Numerics – Alexander von Humboldt Professorship at FAU Erlangen-Nürnberg (Germany) Title: Deep redatuming for PDE and inverse problems Speaker: Prof. Dr. Laurent Demanet Affiliation: MIT – Massachusetts Institute of Technology (USA) Abstract. Neural networks have been leveraged in non-trivial ways in […]
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21MAY 2022
2022-resources
FAU DCN-AvH at #NdW22 Long Night of Sciences
Date: Sat. May 21, 2022 18:00H – 00:00H Title: #NdW22 Long Night of Sciences (Lange Nacht der Wissenschaft) On Saturday May 21th, 2022 the #NdW22 “Lange Nacht der Wissenschaften” (Long Night of Sciences) took place in Erlangen, Fürth, and Nürnberg and our FAU DCN-AvH, Chair for Dynamics, Control, and Numerics – Alexander von Humboldt Professorship […]
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19MAY 2022
2022-resources
Optimal Control Design for Fluid Mixing: from Open-Loop to Closed-Loop
Date: Thu. May 19, 2022 Organized by: Durham University, Seminars in Mathematical Sciences Title: Optimal Control Design for Fluid Mixing: from Open-Loop to Closed-Loop Speaker: Visitor scientist Prof. Dr. Weiwei Hu Affiliation: University of Georgia (USA) Abstract. The question of what velocity fields effectively enhance or prevent transport and mixing, or steer a scalar field […]
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