2 citations · 2 across the 3 of their papers we have counts for
3 papers · 1 filter
Learning differential equation models from stochastic agent-based model simulations
John T. Nardini, Ruth E. Baker, Matthew J. Simpson +1
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology, and epidemiolo…
Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
John H. Lagergren, John T. Nardini, Ruth E. Baker +2
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological syste…
Learning Equations from Biological Data with Limited Time Samples
John T. Nardini, John H. Lagergren, Andrea Hawkins-Daarud +5
Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these me…