Loss formulations for assumption-free neural inference of SDE coefficient functions
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...
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| Published in | NPJ systems biology and applications Vol. 11; no. 1; pp. 22 - 10 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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London
Nature Publishing Group UK
01.03.2025
Nature Publishing Group Nature Portfolio |
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| 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. |
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| AbstractList | 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. 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. 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.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. |
| ArticleNumber | 22 |
| Author | Schmid, Nina Hasenauer, Jan Vaisband, Marc von Bornhaupt, Valentin Abulizi, Izdar |
| Author_xml | – sequence: 1 givenname: Marc surname: Vaisband fullname: Vaisband, Marc organization: Department 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), Paracelsus Medical University, Cancer Cluster Salzburg, Bonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn – sequence: 2 givenname: Valentin surname: von Bornhaupt fullname: von Bornhaupt, Valentin organization: Bonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn – sequence: 3 givenname: Nina surname: Schmid fullname: Schmid, Nina organization: Bonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn – sequence: 4 givenname: Izdar surname: Abulizi fullname: Abulizi, Izdar organization: Bonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn – sequence: 5 givenname: Jan surname: Hasenauer fullname: Hasenauer, Jan email: jan.hasenauer@uni-bonn.de organization: Bonn Center for Mathematical Life Sciences, Life & Medical Sciences (LIMES) Institute, University of Bonn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40025020$$D View this record in MEDLINE/PubMed |
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| Snippet | Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex biological... Abstract Stochastic differential equations (SDEs) are one of the most commonly studied probabilistic dynamical systems, and widely used to model complex... |
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| Title | Loss formulations for assumption-free neural inference of SDE coefficient functions |
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