264 citations · 1.5k across the 33 of their papers we have counts for
3 papers · 1 filter
Self-Supervised Learning of Universal Latent Spaces for Operator Learning
Sepehr Mousavi, Levi Lingsch, Siddhartha Mishra +1
Neural operators fit their representation of functions to the task they are trained on, so every new task rebuilds a reduced space from labeled data. Following the self-supervised…
Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
Sepehr Mousavi, Siddhartha Mishra, Laura De Lorenzis
Neural operators have emerged as powerful surrogates for the solution of partial differential equations (PDEs), yet their ability to handle general, highly variable boundary condit…
NN-EUCLID: deep-learning hyperelasticity without stress data
Prakash Thakolkaran, Akshay Joshi, Yiwen Zheng +3
We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assume…