Deep Neural Networks for Physical 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 2023/2024: The first meeting is October 17 at 9:15 in G22a-218.
Please register with lsf and please also send a mail to and to express your interest.
Literature and possible topics
Link to a list including abstracts.
|[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 ]|
|[KKR23]||V. Kapustsin, U. Kaya, and T. Richter. Error analysis for hybrid finite element / neural network discretizations. submitted, 2023. [ bib | 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 ]|
|[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 ]|
|[LSGW+22]||Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, Shakir Mohamed, and Peter Battaglia. Graphcast: Learning skillful medium-range global weather forecasting, 2022. [ bib | DOI | http | 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 ]|
|[LKA+21]||Z. Li, N. B. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar. Fourier neural operator for parametric partial differential equations. In International Conference on Learning Representations, 2021. [ bib | 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 ]|
|[Bar93]||A.R. Barron. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, 39(3):930--945, 1993. [ bib | Abstract ]|
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