3 citations · 6 across the 5 of their papers we have counts for
5 papers
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…
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in pr…
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…
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
Somayajulu L. N. Dhulipala, Michael D. Shields, Promit Chakroborty +7
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However,…
Machine learning-assisted surrogate construction for full-core fuel performance analysis
Yifeng Che, Joseph Yurko, Koroush Shirvan
Accurately predicting the behavior of a nuclear reactor requires multiphysics simulation of coupled neutronics, thermal-hydraulics and fuel thermo-mechanics. The fuel thermo-mechan…