Teaching - Numerical Mathematics in Applications

Seminar: Scientific Machine Learning for Simulations

Scientific Machine Learning for Simulations

The Seminar Deep Neural Networks for Physical Simulations is offered for Bachelor- and Master students from Computer Science and Mathematics. The seminar takes place each winter term

Date in Winter 2025/2026: The seminar is scheduled Fridays from 11am to 1pm in room G02 20.

Enrollment

We can only provide a limited number of spots for students from computer science and mathematics. At the mooment we are only able to consider some few further requests from mathematics. Please send a mail to and to express your interest. The introduction and selection of talks will take place on Friday October 17.

Participants and talks

Literature and possible topics

The link to a list including abstracts follows shortly

[BSSPS25]	Jostein Barry-Straume, Arash Sarshar, Andrey A. Popov, and Adrian Sandu. Physics-informed neural networks for pde-constrained optimization and control. Communications on Applied Mathematics and Computation, August 2025. (math). [ bib \| DOI \| Abstract ]
[SKCY25]	Adhika Satyadharma, Heng-Chuan Kan, Ming-Jyh Chern, and Chun-Ying Yu. Numerical error estimation with physics informed neural network. Computers & Fluids, 299:106700, August 2025. (math). [ bib \| DOI \| Abstract ]
[APEK⁺25]	Ferran Alet, Ilan Price, Andrew El-Kadi, Dominic Masters, Stratis Markou, Tom R. Andersson, Jacklynn Stott, Remi Lam, Matthew Willson, Alvaro Sanchez-Gonzalez, and Peter Battaglia. Skillful joint probabilistic weather forecasting from marginals, 2025. [ bib \| arXiv \| http \| Abstract ]
[AHB⁺25]	Shreya Agrawal, Mohammed Alewi Hassen, Emmanuel Asiedu Brempong, Boris Babenko, Fred Zyda, Olivia Graham, Di Li, Samier Merchant, Santiago Hincapie Potes, Tyler Russell, Danny Cheresnick, Aditya Prakash Kakkirala, Stephan Rasp, Avinatan Hassidim, Yossi Matias, Nal Kalchbrenner, Pramod Gupta, Jason Hickey, and Aaron Bell. An operational deep learning system for satellite-based high-resolution global nowcasting, 2025. [ bib \| arXiv \| http \| Abstract ]
[FXQ⁺25]	Hang Fan, Yi Xiao, Yongquan Qu, Fenghua Ling, Ben Fei, Lei Bai, and Pierre Gentine. Incorporating multivariate consistency in ml-based weather forecasting with latent-space constraints, 2025. [ bib \| arXiv \| http \| Abstract ]
[BWC24]	Jonghyuk Baek, Yanran Wang, and Jiun-Shyan Chen. N-adaptive ritz method: A neural network enriched partition of unity for boundary value problems. Computer Methods in Applied Mechanics and Engineering, 428:117070, August 2024. (math). [ bib \| DOI \| Abstract ]
[KHB⁺24]	Henrik Krauss, Tim-Lukas Habich, Max Bartholdt, Thomas Seel, and Moritz Schappler. Domain-decoupled physics-informed neural networks with closed-form gradients for fast model learning of dynamical systems. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics, pages 55--66. SCITEPRESS - Science and Technology Publications, 2024. (math). [ bib \| DOI \| Abstract ]
[PSGA⁺24]	Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Tom R. Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, and Matthew Willson. Gencast: Diffusion-based ensemble forecasting for medium-range weather, 2024. [ bib \| arXiv \| http \| Abstract ]
[CSC⁺24]	Guillaume Couairon, Renu Singh, Anastase Charantonis, Christian Lessig, and Claire Monteleoni. Archesweather & archesweathergen: a deterministic and generative model for efficient ml weather forecasting, 2024. [ bib \| arXiv \| http \| Abstract ]
[LVP⁺23]	Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, and Johannes Brandstetter. Pde-refiner: Achieving accurate long rollouts with neural pde solvers, 2023. [ bib \| arXiv \| http \| Abstract ]
[JCXCWEZY22]	Jingrun Chen Jingrun Chen, Xurong Chi Xurong Chi, Weinan E Weinan E, and Zhouwang Yang Zhouwang Yang. Bridging traditional and machine learning-based algorithms for solving pdes: The random feature method. Journal of Machine Learning, 1(3):268--298, January 2022. (math). [ bib \| DOI \| Abstract ]