Seminar: Scientific Machine Learning for Simulations
Scientific Machine Learning for Simulations
Christian Lessig and Thomas Richter
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 2024/2025: The first meeting for registration is October 15 at 11:15 in G02-20.
Enrollment
Please register with lsf and please also send a mail to and to express your interest.
Participants and talks
- Lukas Eichel. Scaling Transformer Neural Networks
- Sofia Yuvchenko. A functional approach to interpreting the role of the adjoint equation in machine learning
- Dominik Apel. Accurate medium-range global weather forecasting with 3D neural networks
- Moritz Pötzsch. Global atmospheric data assimilation with multi-modal masked autoencoders
- Jörn Papenbroock. Evolutionary-scale prediction of atomic level protein structure with a language model
- Allipilli Harshitha. Global atmospheric data assimilation with multi-modal masked autoencoders
- Paul Matschoss. Error Estimates for the Deep Ritz Method with Boundary Penalty
- Max-Fabian Ksoll. Aurora: A foundation model of the atmosphere
Literature and possible topics
Link to a list including abstracts.
| [VH24] | P. Trent Vonich and Gregory J. Hakim. Predictability limit of the 2021 pacific northwest heatwave from deep-learning sensitivity analysis, 2024. [ bib | arXiv | http ] |
| [KYL+24] | Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, and Stephan Hoyer. Neural general circulation models for weather and climate. Nature, 2024. [ bib | DOI | http | Abstract ] |
| [VDM+24] | Thomas J. Vandal, Kate Duffy, Daniel McDuff, Yoni Nachmany, and Chris Hartshorn. Global atmospheric data assimilation with multi-modal masked autoencoders, 2024. [ bib | arXiv | http ] |
| [VMT+24] | Anna Vaughan, Stratis Markou, Will Tebbutt, James Requeima, Wessel P. Bruinsma, Tom R. Andersson, Michael Herzog, Nicholas D. Lane, Matthew Chantry, J. Scott Hosking, and Richard E. Turner. Aardvark weather: end-to-end data-driven weather forecasting, 2024. [ bib | arXiv | http ] |
| [HM24] | Gregory J. Hakim and Sanjit Masanam. Dynamical tests of a deep learning weather prediction model. Artificial Intelligence for the Earth Systems, 3(3):e230090, 2024. [ bib | DOI | http ] |
| [BBL+24] | Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Tambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, and Paris Perdikaris. Aurora: A foundation model of the atmosphere, 2024. [ bib | arXiv ] |
| [RKL+24] | Thomas Rackow, Nikolay Koldunov, Christian Lessig, Irina Sandu, Mihai Alexe, Matthew Chantry, Mariana Clare, Jesper Dramsch, Florian Pappenberger, Xabier Pedruzo-Bagazgoitia, Steffen Tietsche, and Thomas Jung. Robustness of ai-based weather forecasts in a changing climate, 2024. [ bib | arXiv | http ] |
| [FMS23] | Imre Fekete, András Molnár, and Péter L. Simon. A functional approach to interpreting the role of the adjoint equation in machine learning. Results in Mathematics, 79(1), December 2023. Seminar 2024/2025. [ bib | DOI | Abstract ] |
| [TNF+23] | Derick Nganyu Tanyu, Jianfeng Ning, Tom Freudenberg, Nick Heilenkötter, Andreas Rademacher, Uwe Iben, and Peter Maass. Deep learning methods for partial differential equations and related parameter identification problems. Inverse Problems, 39(10):103001, aug 2023. [ bib | DOI | Abstract ] |
| [MBC+23] | Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, and Mike Pritchard. Generative residual diffusion modeling for km-scale atmospheric downscaling, 2023. [ bib | arXiv | Abstract ] |
| [BXZ+23] | Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. Accurate medium-range global weather forecasting with 3d neural networks. Nature, 2023. [ bib | DOI | http | Abstract ] |
| [NSB+23] | Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian Foster, and Aditya Grover. Scaling transformer neural networks for skillful and reliable medium-range weather forecasting, 2023. [ bib | arXiv | http ] |
| [HÃS23] | Paul Häusner, Ozan Öktem, and Jens Sjölund. Neural incomplete factorization: learning preconditioners for the conjugate gradient method, 2023. Seminar 2024/2025. [ bib | DOI | Abstract ] |
| [MZ23] | Johannes Müller and Marius Zeinhofer. Achieving high accuracy with pinns via energy natural gradients, 2023. Seminar 2024/2025. [ bib | DOI | Abstract ] |
| [LAR+22] | Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, and Alexander Rives. Evolutionary-scale prediction of atomic level protein structure with a language model. bioRxiv, 2022. [ bib | DOI | arXiv | http | Abstract ] |
| [LDF+22] | Yang Li, Haiyu Dong, Zuliang Fang, Jonathan Weyn, and Pete Luferenko. Super-resolution probabilistic rain prediction from satellite data using 3d u-nets and earthformers, 2022. [ bib | arXiv | Abstract ] |
| [DHP21] | Ronald DeVore, Boris Hanin, and Guergana Petrova. Neural network approximation. Acta Numerica, 30:327--444, may 2021. [ bib | DOI | Abstract ] |
| [MZ21] | Johannes Müller and Marius Zeinhofer. Error estimates for the deep ritz method with boundary penalty. March 2021. [ bib | DOI | arXiv | Abstract ] |
| [KLM21] | Nikola Kovachki, Samuel Lanthaler, and Siddhartha Mishra. On universal approximation and error bounds for fourier neural operators. 2021. [ bib | DOI | http | Abstract ] |
| [HLZ+20] | Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, and Hyunjik Kim. Lietransformer: Equivariant self-attention for lie groups, 2020. [ bib | DOI | http | Abstract ] |
| [SV18] | Kevin Scaman and Aladin Virmaux. Lipschitz regularity of deep neural networks: analysis and efficient estimation. May 2018. [ bib | DOI | arXiv | Abstract ] |
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