6 citations · 8 across the 3 of their papers we have counts for
3 papers
Learning Object Properties Using Robot Proprioception via Differentiable Robot-Object Interaction
Peter Yichen Chen, Chao Liu, Pingchuan Ma +5
Differentiable simulation has become a powerful tool for system identification. While prior work has focused on identifying robot properties using robot-specific data or object pro…
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
Blake Bullwinkel, Dylan Randle, Pavlos Protopapas +1
Solutions to differential equations are of significant scientific and engineering relevance. Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving…
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
Dylan Randle, Pavlos Protopapas, David Sondak
Solutions to differential equations are of significant scientific and engineering relevance. Recently, there has been a growing interest in solving differential equations with neur…