15 citations · 20 across the 5 of their papers we have counts for
5 papers
Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks
Anthony Baez, Wang Zhang, Ziwen Ma +3
We propose a novel projection method that guarantees the conservation of integral quantities in Physics-Informed Neural Networks (PINNs). While the soft constraint that PINNs use t…
Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Anthony Baez, Wang Zhang, Ziwen Ma +3
Physics-informed neural networks (PINNs) incorporate physical laws into their training to efficiently solve partial differential equations (PDEs) with minimal data. However, PINNs…
One step closer to unbiased aleatoric uncertainty estimation
Wang Zhang, Ziwen Ma, Subhro Das +4
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making. In the deep learning field, the uncertainties…
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Wang Zhang, Tsui-Wei Weng, Subhro Das +3
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws. This pape…
Robust Deep Reinforcement Learning through Adversarial Loss
Tuomas Oikarinen, Wang Zhang, Alexandre Megretski +2
Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent's inputs, which raises concerns about deploying suc…