198 citations · 606 across the 20 of their papers we have counts for
3 papers · 2 filters
Random Weight Factorization Improves the Training of Continuous Neural Representations
Sifan Wang, Hanwen Wang, Jacob H. Seidman +1
Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to captur…
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
Arka Daw, Jie Bu, Sifan Wang +2
Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution…
Respecting causality is all you need for training physics-informed neural networks
Sifan Wang, Shyam Sankaran, Paris Perdikaris
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical systems whose solution exhi…