135 citations · 473 across the 31 of their papers we have counts for
3 papers · 1 filter
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows
Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis +1
Physics-informed neural networks (PINNs) provide a framework to build surrogate models for dynamical systems governed by differential equations. During the learning process, PINNs…
Improving Gradient Computation for Differentiable Physics Simulation with Contacts
Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey +1
Differentiable simulation enables gradients to be back-propagated through physics simulations. In this way, one can learn the dynamics and properties of a physics system by gradien…