Model Reduction and Surrogate Modeling (MORE)

19-23 Sep 2022 Berlin (Germany)

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Program

Week Mon. 19 Tue. 20 Wed. 21 Thu. 22 Fri. 23 List

‹ Thursday, September 22, 2022 ›

08:00

09:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

›8:30 (45min)

Plenary
Chair: Ralf Zimmermann

› H 1012

8:30 - 9:15 (45min)

Plenary

H 1012

Chair: Ralf Zimmermann

› Digital Twins through Reduced Order Models and Machine Learning - Jan S. Hesthaven, Ecole Polytechnique Fédérale de Lausanne 08:30-09:15 (45min)

›9:20 (1h15)

Session 5
Chair: Ralf Zimmermann

› H 1012

9:20 - 10:35 (1h15)

Session 5

H 1012

Chair: Ralf Zimmermann

› Optimization based model order reduction for stochastic systems - Melina Freitag, Institute of Mathematics 09:20-09:45 (25min)

› Balanced Truncation for Bayesian Inference - Elizabeth Qian, California Institute of Technology 09:45-10:10 (25min)

› Semi-supervised Invertible DeepONets for Bayesian Inverse Problems - Sebastian Kaltenbach, Technical University of Munich 10:10-10:35 (25min)

›10:35 (25min)

Coffee break

› H 3005/3006

10:35 - 11:00 (25min)

Coffee break

H 3005/3006

›11:00 (1h40)

Parallel Session 6a
Chair: Tobias Breiten

› H 1012

11:00 - 12:40 (1h40)

Parallel Session 6a

H 1012

Chair: Tobias Breiten

› Structured barycentric forms and their application to iterative data-driven model reduction of second-order systems - Ion Victor Gosea, Max-Planck-Institute Magdeburg 11:00-11:25 (25min)

› Structured L2-Optimal Parametric Model Order Reduction - Petar Mlinarić, Virginia Tech 11:25-11:50 (25min)

› High Order Approximations of the Operator Lyapunov Equation Have Low Rank - Luka Grubisic, University of Zagreb, Faculty of Science 11:50-12:15 (25min)

› Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models - Stefania Fresca, Politecnico di Milano 12:15-12:40 (25min)

›11:00 (1h40)

Parallel Session 6b
Chair: Mario Ohlberger

› H 1058

11:00 - 12:40 (1h40)

Parallel Session 6b

H 1058

Chair: Mario Ohlberger

› Hybrid fluid/particle methods for kinetic equations describing neutral particles in nuclear fusion plasma-edge modelling - Vince Maes, KU Leuven, Department of Computer Science 11:00-11:25 (25min)

› Model Order Reduction in Contour Integral Methods for parametric PDEs - Mattia Manucci, Gran Sasso Science Institute 11:25-11:50 (25min)

› Inf-Sup-Constant-Free Error Estimation for Linear Parametric Systems - Sridhar Chellappa, Max Planck Institute for Dynamics of Complex Technical Systems 11:50-12:15 (25min)

› Parametric reduced order modelling for transport dominated systems via shifted POD deep learning models - Shubhaditya Burela, Technical University of Berlin 12:15-12:40 (25min)

›12:40 (1h20)

Lunch

12:40 - 14:00 (1h20)

Lunch

›14:00 (45min)

Plenary
Chair: Serkan Gugercin

› H 1012

14:00 - 14:45 (45min)

Plenary

H 1012

Chair: Serkan Gugercin

› Port-Hamiltonian systems -- from a general modelling wishlist to surrogate models with guarantees - Benjamin Unger, SC Sim Tech, University of Stuttgart 14:00-14:45 (45min)

›14:50 (1h15)

Session 6
Chair: Serkan Gugercin

› H 1012

14:50 - 16:05 (1h15)

Session 6

H 1012

Chair: Serkan Gugercin

› Registration-based model reduction of parameterized PDEs with spatio-parameter adaptivity - tommaso taddei, Inria Bordeaux South-West 14:50-15:15 (25min)

› A certified wavelet-based physics-informed neural network for nonlinear model reduction of parameterized partial differential equations - Lewin Ernst, Institute for Numerical Mathematics, Ulm University 15:15-15:40 (25min)

› Neural networks investigation of bifurcating phenomena in fluid-dynamics - Federico Pichi, EPFL, École Polytechnique Fédérale de Lausanne, MCSS, Route Cantonale, 1015, Lausanne, Switzerland. 15:40-16:05 (25min)

›16:05 (25min)

Coffee break

› H 3005/3006

16:05 - 16:30 (25min)

Coffee break

H 3005/3006

›16:30 (1h15)

Session 7
Chair: Benjamin Peherstorfer

› H 1012

16:30 - 17:45 (1h15)

Session 7

H 1012

Chair: Benjamin Peherstorfer

› Deep Orthogonal Decomposition via Mesh-Informed Neural Networks for Reduced Order Modeling of parametrized PDEs - Nicola Franco, Modeling and Scientific Computing [Milano] 16:30-16:55 (25min)

› An adaptive hierarchy of certified machine learning and reduced basis surrogates for parametrized PDEs - Felix Schindler, Mathematics Münster, University of Münster 16:55-17:20 (25min)

› Multiscale modeling of heterogeneous structures based on a localized model order reduction approach - Philipp Diercks, Bundesanstalt für Materialforschung und -prüfung (BAM) 17:20-17:45 (25min)

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