mlsanityData to 7 Oct 2026

Non-Negative Universal Differential Equations With Applications in Systems Biology

arXiv 2406.14246 ↗First posted 20 Jun 2024q-bio.QM cs.LG math.DScited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 20 Jun 2024.

Builds on

3 of 19 references.

References · 3

  1. 2019Augmented Neural ODEscited by 396
  2. 2020Universal Differential Equations for Scientific Machine Learningcited by 345
  3. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 311

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamicscited by 0
  2. 2026Bayesian Symbolic Regression for Missing Physicscited by 0
  3. 2026Experimental Design for Missing Physicscited by 0
  4. 2026Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programmingcited by 0