research
∙
03/20/2023
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data
In complex large-scale systems such as climate, important effects are ca...
share
research
∙
04/22/2022
Error-in-variables modelling for operator learning
Deep operator learning has emerged as a promising tool for reduced-order...
research
∙
10/22/2021
Fractional Modeling in Action: A Survey of Nonlocal Models for Subsurface Transport, Turbulent Flows, and Anomalous Materials
Modeling of phenomena such as anomalous transport via fractional-order d...
research
∙
07/07/2021
Probabilistic partition of unity networks: clustering based deep approximation
Partition of unity networks (POU-Nets) have been shown capable of realiz...
research
∙
01/12/2021
Analysis of Anisotropic Nonlocal Diffusion Models: Well-posedness of Fractional Problems for Anomalous Transport
We analyze the well-posedness of an anisotropic, nonlocal diffusion equa...
research
∙
12/22/2020
Gaussian Process Regression constrained by Boundary Value Problems
We develop a framework for Gaussian processes regression constrained by ...
research
∙
06/16/2020
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Gaussian process regression is a popular Bayesian framework for surrogat...
research
∙
05/17/2020
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data
A key challenge to nonlocal models is the analytical complexity of deriv...
research
∙
08/02/2018