research
∙
04/22/2022
Error-in-variables modelling for operator learning
Deep operator learning has emerged as a promising tool for reduced-order...
share
research
∙
01/27/2021
Partition of unity networks: deep hp-approximation
Approximation theorists have established best-in-class optimal approxima...
research
∙
12/09/2020
Thermodynamically consistent physics-informed neural networks for hyperbolic systems
Physics-informed neural network architectures have emerged as a powerful...
research
∙
09/25/2020
A physics-informed operator regression framework for extracting data-driven continuum models
The application of deep learning toward discovery of data-driven models ...
research
∙
06/17/2020
A block coordinate descent optimizer for classification problems exploiting convexity
Second-order optimizers hold intriguing potential for deep learning, but...
research
∙
12/10/2019
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint
Motivated by the gap between theoretical optimal approximation rates of ...
research
∙
09/07/2019
GMLS-Nets: A framework for learning from unstructured data
Data fields sampled on irregularly spaced points arise in many applicati...
research
∙
10/19/2018
