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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.
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.
Tensor Basis Gaussian Process Models of Hyperelastic Materials
Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential.
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