Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design
Many biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data. Biologically-informed neural networks (BINNs) seek to address this challenge by embedding differential equations into neural network training, enabling constitutive operators to be recovered directly from sparse and noisy observations. However, the extent to which operator recovery depends on architectural design, optimisation strategy and the information within the data is not yet well understood. We present an empirical study of how these factors influence mechanistic inference using BINNs applied to one-dimensional advection-diffusi...
| Authors | Rebecca M. Crossley, Yuan Yin, Sarah L. Waters et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 8 July 2026 |
| Last updated at source | 2 October 2026 |
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