663 citations · 1.3k across the 32 of their papers we have counts for
32 papers
On Active Learning for Gaussian Process-based Global Sensitivity Analysis
Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli +3
This paper explores the application of active learning strategies to adaptively learn Sobol indices for global sensitivity analysis. We demonstrate that active learning for Sobol i…
Learning thermodynamically constrained equations of state with uncertainty
Himanshu Sharma, Jim A. Gaffney, Dimitrios Tsapetis +1
Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material's thermodynamic state variables -- specifically pressure, vol…
UQpy v4.1: Uncertainty Quantification with Python
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis +9
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured t…
Learning in latent spaces improves the predictive accuracy of deep neural operators
Katiana Kontolati, Somdatta Goswami, George Em Karniadakis +1
Operator regression provides a powerful means of constructing discretization-invariant emulators for partial-differential equations (PDEs) describing physical systems. Neural opera…
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion
Lukáš Novák, Michael D. Shields, Václav Sadílek +1
The paper presents a novel methodology to build surrogate models of complicated functions by an active learning-based sequential decomposition of the input random space and constru…
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation
Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che +4
Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is s…