Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design
This research uses Biologically-informed Neural Networks (BINNs) to infer unknown biological mechanisms from sparse, noisy data by embedding governing differential equations into neural network training. The key finding is that reliable mechanistic recovery depends on balancing model expressivity, learning rate, loss weighting, and batch size rather than maximizing any single factor.