5 papers
Spatial Model Selection and Uncertainty Quantification: Comparing Continuous and Discrete Wound Healing Models
John T. Nardini, Jana L. Gevertz
All data-driven modeling tasks (e.g., parameter estimation, uncertainty quantification, and data forecasting) require the selection of a mathematical model. An overlooked aspect of…
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…
SSRCA: a novel machine learning pipeline to perform sensitivity analysis for agent-based models
Edward H. Rohr, John T. Nardini
Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior. Modelers employ sensitivity analysis (SA) algorit…
Quantifying topological features and irregularities in zebrafish patterns using the sweeping-plane filtration
Nour Khoudari, John Nardini, Alexandria Volkening
Complex patterns emerge across a wide range of biological systems. While such patterns often exhibit remarkable robustness, variation and irregularity exist at multiple scales and…