31 citations · 33 across the 4 of their papers we have counts for
4 papers
Neuro-symbolic partial differential equation solver
Pouria Mistani, Samira Pakravan, Rajesh Ilango +2
We present a highly scalable strategy for developing mesh-free neuro-symbolic partial differential equation solvers from existing numerical discretizations found in scientific comp…
Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations
Benjamin Wu, Oliver Hennigh, Jan Kautz +2
Physics-informed neural networks allow models to be trained by physical laws described by general nonlinear partial differential equations. However, traditional architectures strug…
Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials
B V S S Bharadwaja, Mohammad Amin Nabian, Bharatkumar Sharma +2
In this work, a model based on the Physics - Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ)…
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian +7
We present SimNet, an AI-driven multi-physics simulation framework, to accelerate simulations across a wide range of disciplines in science and engineering. Compared to traditional…