1 code implementation • 13 Feb 2019 • John Lagergren, John T. Nardini, G. Michael Lavigne, Erica M. Rutter, Kevin B. Flores
We analyze the performance in utilizing previous methods to denoise data for the task of discovering the governing system of partial differential equations (PDEs).
Dynamical Systems
1 code implementation • 26 May 2020 • John H. Lagergren, John T. Nardini, Ruth E. Baker, Matthew J. Simpson, Kevin B. Flores
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data.
1 code implementation • 16 Nov 2020 • John T. Nardini, Ruth E. Baker, Matthew J. Simpson, Kevin B. Flores
We propose that methods from the equation learning field provide a promising, novel, and unifying approach for agent-based model analysis.
Dynamical Systems
1 code implementation • 2 Jan 2021 • John T. Nardini, Bernadette J. Stolz, Kevin B. Flores, Heather A. Harrington, Helen M. Byrne
Here we simulate the Anderson-Chaplain model of angiogenesis at different parameter values and quantify the vessel architectures of the resulting synthetic data.