multifidelity

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, …