Loss formulations for assumption-free neural inference of SDE coefficient functions
Abstract Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological processes. Building upon the previously introduced idea of performing inference of dynamical systems by parametrising their coefficient...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00500-6 |
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| _version_ | 1849767274375806976 |
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| author | Marc Vaisband Valentin von Bornhaupt Nina Schmid Izdar Abulizi Jan Hasenauer |
| author_facet | Marc Vaisband Valentin von Bornhaupt Nina Schmid Izdar Abulizi Jan Hasenauer |
| author_sort | Marc Vaisband |
| collection | DOAJ |
| description | Abstract Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological processes. Building upon the previously introduced idea of performing inference of dynamical systems by parametrising their coefficient functions via neural networks, we propose a novel formulation for an optimisation objective that combines simulation-based penalties with pseudo-likelihoods. This greatly improves prediction performance compared to the state-of-the-art, and makes it possible to learn a wide variety of dynamics without any prior assumptions on analytical structure. |
| format | Article |
| id | doaj-art-49fe3ec723e94a8f8fbb0ec333b22b0d |
| institution | DOAJ |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-49fe3ec723e94a8f8fbb0ec333b22b0d2025-08-20T03:04:16ZengNature Portfolionpj Systems Biology and Applications2056-71892025-03-0111111010.1038/s41540-025-00500-6Loss formulations for assumption-free neural inference of SDE coefficient functionsMarc Vaisband0Valentin von Bornhaupt1Nina Schmid2Izdar Abulizi3Jan Hasenauer4Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR)Bonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of BonnBonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of BonnBonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of BonnBonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of BonnAbstract Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological processes. Building upon the previously introduced idea of performing inference of dynamical systems by parametrising their coefficient functions via neural networks, we propose a novel formulation for an optimisation objective that combines simulation-based penalties with pseudo-likelihoods. This greatly improves prediction performance compared to the state-of-the-art, and makes it possible to learn a wide variety of dynamics without any prior assumptions on analytical structure.https://doi.org/10.1038/s41540-025-00500-6 |
| spellingShingle | Marc Vaisband Valentin von Bornhaupt Nina Schmid Izdar Abulizi Jan Hasenauer Loss formulations for assumption-free neural inference of SDE coefficient functions npj Systems Biology and Applications |
| title | Loss formulations for assumption-free neural inference of SDE coefficient functions |
| title_full | Loss formulations for assumption-free neural inference of SDE coefficient functions |
| title_fullStr | Loss formulations for assumption-free neural inference of SDE coefficient functions |
| title_full_unstemmed | Loss formulations for assumption-free neural inference of SDE coefficient functions |
| title_short | Loss formulations for assumption-free neural inference of SDE coefficient functions |
| title_sort | loss formulations for assumption free neural inference of sde coefficient functions |
| url | https://doi.org/10.1038/s41540-025-00500-6 |
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