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Found 3 papers, 0 papers with code
Data-driven Discovery of Chemotactic Migration of Bacteria via Machine Learning
The resulting data-driven PDE can then be simulated to reproduce/predict computational or experimental bacterial density profile data, and estimate the underlying (unmeasured) chemonutrient field evolution.
Limits of Entrainment of Circadian Neuronal Networks
In this work, we study a modern computational neuroscience model to determine the limits of circadian synchronization to external light signals of different frequency and duty cycle.
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data
We exploit Automatic Relevance Determination (ARD) within a Gaussian Process framework for the identification of a parsimonious set of collective observables that parametrize the law of the effective PDEs.
