13 citations · 21 across the 5 of their papers we have counts for
5 papers
Geometry-aware PINNs for Turbulent Flow Prediction
Shinjan Ghosh, Julian Busch, Georgia Olympia Brikis +1
Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especi…
Using Parametric PINNs for Predicting Internal and External Turbulent Flows
Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis +1
Computational fluid dynamics (CFD) solvers employing two-equation eddy viscosity models are the industry standard for simulating turbulent flows using the Reynolds-averaged Navier-…
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…
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis
In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator,…