13 citations · 13 across the 6 of their papers we have counts for
6 papers
PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations
Weiheng Zhong, Jing Bi, Victor Oancea +1
While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by high memory consumption and…
PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks
Nahil Sobh, Rini Jasmine Gladstone, Hadi Meidani
Physics-Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by embedding governing equations and boundary/initial conditions into the loss function. Howeve…
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations
Rini Jasmine Gladstone, Hadi Meidani
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inpu…
Physics-Informed Geometry-Aware Neural Operator
Weiheng Zhong, Hadi Meidani
Engineering design problems often involve solving parametric Partial Differential Equations (PDEs) under variable PDE parameters and domain geometry. Recently, neural operators hav…
Physics-informed Discretization-independent Deep Compositional Operator Network
Weiheng Zhong, Hadi Meidani
Solving parametric Partial Differential Equations (PDEs) for a broad range of parameters is a critical challenge in scientific computing. To this end, neural operators, which \text…
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Rini J. Gladstone, Mohammad A. Nabian, N. Sukumar +2
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorpor…