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Program

Tuesday, March 11, 2025

Opening (09:20 – 09:30)

09:30 – 10:10: Jakob Zech, Heidelberg University.
Statistical Learning Theory for Neural Operators

10:10 – 10:50: Kansei Ushiyama, The University of Tokyo.
Performance estimation problems for convergence rate analysis of continuous-time models for optimization algorithms

Coffee break (10:50 – 11:20)

11:20 – 12:00: Eloi Martinet, University of Würzburg.
Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs

Lunch break (12:00 – 14:00)

14:00 – 14:40: Amy Braverman, Jet Propulsion Laboratory, California Institute of Technology.
Simulation-based Uncertainty Quantification

14:40 – 15:20: Tan Bui-Thanh, The University of Texas at Austin.
Learn2Solve: A Deep Learning Framework for Real-Time Solutions of forward, inverse, and UQ Problems

Coffee break (15:20 – 16:00)

16:00 – 16:40: Holger Fröning, Heidelberg University.
Bayesian Machines: Unlocking the Potential of Bayesian Neural Networks for Enhanced Uncertainty Reasoning

16:40 – 17:20: Michael Feischl, TU Wien.
Towards optimal hierarchical training of neural networks

Wednesday, March 12, 2025

09:30 – 10:10: Dejan Slepčev, Carnegie Mellon University.
Interacting particle dynamics for sampling in high dimensions

10:10 – 10:50: Damien Garreau, University of Würzburg.
Are Ensembles Getting Better all the Time?

Coffee break (10:50 – 11:20)

11:20 – 12:00: Petr Knobloch, Charles University, Prague.
Computation of stabilization parameters using machine learning

Lunch break (12:00 – 14:00)

14:00 – 14:40: Kathrin Hellmuth, University of Würzburg.
Experimental Design for Inverse Problems through Random Sampling of the Gauss-Newton Hessian

14:40 – 15:20: Zhi-Song Liu, Lappeenranta-Lahti University of Technology.
Iterative Inversion for 3D Point Clouds Upsampling

Coffee break (15:20 – 16:00)

16:00 – 16:40: Satoru Iwasaki, Osaka University.
Surrogate Modeling for Thin Domain PDEs via Reduction Theory

16:40 – 17:20: Jon Cockayne, University of Southampton.
Calibrated Computation-Aware Gaussian Processes

Dinner (18:30)

Thursday, March 13, 2025

09:30 – 10:10: Andreas Hauptmann, University of Oulu, Finland; University College London, UK.
Learned iterative reconstructions with applications to linear and nonlinear inverse problems

10;10 – 10:50: Alice Oberacker, Saarland University.
Reducing Motion Artifacts in Nano-CT Imaging with a Learned RESESOP-Kaczmarz Method

Coffee break (10:50 – 11:20)

11:20 – 12:00: Chen Song, ABB Corporate Research Center.
Physics-Informed Machine Learning in Non-invasive Measurement Techniques

Lunch break (12:00 – 14:00)

14:00 – 14:40: Kota Takeda, Kyoto University.
Error analysis and numerical issues in data assimilation

14:40 – 15:20: Yuka Hashimoto, NTT Network Service Systems Laboratories / RIKEN AIP.
Reproducing kernel Hilbert C*-module and spectral truncation kernel

Closing (15:20 – 15:30)