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physics.comp-ph2021★ 12 cited
Explicit physics-informed neural networks for non-linear upscaling closure: the case of transport in tissues
Ehsan Taghizadeh, Helen M. Byrne, Brian D. Wood
In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in a multiscale tissue. Th…
physics.comp-ph2015★ 36 cited
Hybrid approaches for multiple-species stochastic reaction-diffusion models
Fabian Spill, Pilar Guerrero, Tomas Alarcon +2
Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual ent…