surrogate models

Gradient-free active subspace recovery using deep neural networks

A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for the construction of accurate surrogate models. Such efforts are particularly important to applications constrained by …

Deep neural networks for multifidelity uncertainty quantification

A common scenario in computational science is the availability of a suite of simulators solving the same physical problem, such that the simulators are at varying levels of cost/fidelity. High fidelity simulators are more accurate but are …

Learning nonlinear correlations between multifidelity models using deep neural networks

A typical scenario in computational science is the availability of a suite of simulators the solve the same physical problem at varying degrees of accuracy (or *fidelity*). Higher fidelity solvers adhere to the underlying physics more faithfully, …

Solving multiscale stochastic partial differential equations using deep neural networks

The applicability of traditional methods for stochastic partial differential equations (SPDEs) including Monte Carlo (MC) methods (and its variants), operator based methods, moment methods and generalized polynomial chaos (gPC) is typically …

Probablistic active subspaces

Gaussian process regression (GPR) is an immensely popular choice for computational scientists as a surrogate model for various uncertainty quantification tasks. However, it's appliacability is limited by it's poor scalability to high input …