Apr 23, 2022 · Abstract:Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function.
Apr 25, 2022 · We introduce competitive physics informed networks where two neural networks solve a partial differential equation by playing a zero-sum game.
CPINN uses an adversarial architecture to train Physics Informed Neural Networks (PINNs) against a discriminator in a zero-sum minimax game to reach higher ...
Competitive Physics Informed Networks. Q Zeng, Y Kothari, SH Bryngelson, F Schäfer. International Conference on Learning Representations (ICLR) 2023, 2023. 26 ...
May 7, 2024 · This study investigates the potential accuracy boundaries of physics-informed neural networks, contrasting their approach with previous similar works and ...
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Co-authors. View all ; Competitive physics informed networks. Q Zeng, Y Kothari, SH Bryngelson, F Schäfer. arXiv preprint arXiv:2204.11144, 2022. 27, 2022.
This work presents a surrogate modeling strategy for predicting radiative heat transfer. The proposed model leverages physics-informed deep neural operator ...
The workshop aims to discuss the use of High-Performance Computing (HPC) to support monitoring the world for any nuclear explosions.
Jul 15, 2024 · Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological ...
Jun 17, 2024 · Two class of 2024 engineering PhD graduates are moving on to highly competitive postdoctoral research and tenure-track faculty positions.