Search Results for author:
Found 7 papers, 0 papers with code
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 caused by a combination of confounding processes that are not fully observable.
Error-in-variables modelling for operator learning
We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems.
Probabilistic partition of unity networks: clustering based deep approximation
We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimization of a maximum likelihood loss.
Gaussian Process Regression constrained by Boundary Value Problems
The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available.
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources.
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data
A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori.
Machine Learning of Space-Fractional Differential Equations
We extend this framework to linear space-fractional differential equations.
