Search Results for author: John T. Nardini

Found 5 papers, 5 papers with code

Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis

1 code implementation2 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.

Topological Data Analysis

Learning differential equation models from stochastic agent-based model simulations

1 code implementation16 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

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

1 code implementation26 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.

Learning partial differential equations for biological transport models from noisy spatiotemporal data

1 code implementation13 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

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