research
∙
09/01/2023
Multi-fidelity reduced-order surrogate modeling
High-fidelity numerical simulations of partial differential equations (P...
share
research
∙
05/19/2023
Bayesian approach to Gaussian process regression with uncertain inputs
Conventional Gaussian process regression exclusively assumes the existen...
research
∙
02/16/2023
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Uncertainty quantification (UQ) tasks, such as sensitivity analysis and ...
research
∙
08/27/2022
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
This work proposes a Stochastic Variational Deep Kernel Learning method ...
research
∙
08/05/2022
Multi-fidelity surrogate modeling using long short-term memory networks
When evaluating quantities of interest that depend on the solutions to d...
research
∙
04/22/2022
Bayesian operator inference for data-driven reduced-order modeling
This work proposes a Bayesian inference method for the reduced-order mod...
research
∙
07/25/2021
A brief note on understanding neural networks as Gaussian processes
As a generalization of the work in [Lee et al., 2017], this note briefly...
research
∙
02/26/2021
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Highly accurate numerical or physical experiments are often time-consumi...
research
∙
10/18/2020