Non-Negative Universal Differential Equations With Applications in Systems Biology
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916506662600704 |
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| author | Philipps, Maren Körner, Antonia Vanhoefer, Jakob Pathirana, Dilan Hasenauer, Jan |
| author_facet | Philipps, Maren Körner, Antonia Vanhoefer, Jakob Pathirana, Dilan Hasenauer, Jan |
| contents | Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14246 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-Negative Universal Differential Equations With Applications in Systems Biology Philipps, Maren Körner, Antonia Vanhoefer, Jakob Pathirana, Dilan Hasenauer, Jan Quantitative Methods Machine Learning Dynamical Systems Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs. |
| title | Non-Negative Universal Differential Equations With Applications in Systems Biology |
| topic | Quantitative Methods Machine Learning Dynamical Systems |
| url | https://arxiv.org/abs/2406.14246 |