A likelihood-based framework for simultaneously learning both noise and growth dynamics using biologically-informed neural networks
This framework extends Biologically-Informed Neural Networks to simultaneously learn population growth dynamics and noise structure. It models observation noise as density-dependent, allowing the network to discover a learnable scaling law for noise magnitude directly from data. This moves beyond fixed assumptions about Gaussian noise, providing a more accurate reconstruction of underlying biological processes.