176 citations · 392 across the 14 of their papers we have counts for
3 papers · 1 filter
Hyperparameter selection for equation learning with biologically-informed neural networks
William Lavery, Jodie A. Cochrane, John T. Nardini +1
Biologically-informed neural networks (BINNs) have emerged as a flexible subclass of physics-informed neural networks (PINNs) for learning terms in partial differential equations f…
Physics-Informed Neural Networks for Biological Reaction-Diffusion Systems
William Lavery, Jodie A. Cochrane, Christian Olesen +3
Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-informed neural networks (BINNs)…
Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)
Maria-Veronica Ciocanel, John T. Nardini, Kevin B. Flores +3
Agent-based modeling (ABM) is a powerful tool for understanding self-organizing biological systems, but it is computationally intensive and often not analytically tractable. Equati…