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Predicting ordinary differential equations with transformers.
In: (Proceedings of Machine Learning Research). 1269 Law St, San Diego, Ca, United States: Jmlr-journal Machine Learning Research, 2023. 25 ( ; 202)
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observations of a single solution trajectory. We demonstrate in extensive empirical evaluations that our model performs better or on par with existing methods in terms of accurate recovery across various settings. Moreover, our method is efficiently scalable: after one-time pretraining on a large set of ODEs, we can infer the governing law of a new observed solution in a few forward passes of the model.
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Publication type
Article: Conference contribution
ISSN (print) / ISBN
2640-3498
Conference Title
Proceedings of Machine Learning Research
Quellenangaben
Volume: 202,
Pages: 25
Publisher
Jmlr-journal Machine Learning Research
Publishing Place
1269 Law St, San Diego, Ca, United States
Non-patent literature
Publications
Institute(s)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Computational Biology (ICB)
Institute of Computational Biology (ICB)
Grants
Helmholtz Association's Initiative and Networking Fund on the HAICORE@FZJpartition
Helmholtz Association under the joint research school "Munich School for Data Science - MUDS"
Helmholtz Association under the joint research school "Munich School for Data Science - MUDS"