13 citations · 16 across the 8 of their papers we have counts for
4 papers · 1 filter
Physics-Informed Polynomial Chaos Expansions
Lukáš Novák, Himanshu Sharma, Michael D. Shields
Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is…
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 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…
Bayesian Inference with Latent Hamiltonian Neural Networks
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS) which automatically decides the end…