1.4k citations · 5.1k across the 32 of their papers we have counts for
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Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing
Salah A Faroughi, Nikhil Pawar, Celio Fernandes +4
Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, s…
ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
Sukirt Thakur, Maziar Raissi, Arezoo M. Ardekani
Viscoelastic fluids are a class of fluids that exhibit both viscous and elastic nature. Modelling such fluids requires constitutive equations for the stress, and choosing the most…
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Salvatore Cuomo, Vincenzo Schiano di Cola, Fabio Giampaolo +3
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itsel…