42 citations · 43 across the 4 of their papers we have counts for
4 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…
Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel
Somayajulu L. N. Dhulipala, Promit Chakroborty, Michael D. Shields +3
The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety…
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,…
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation
S. L. N. Dhulipala, M. D. Shields, B. W. Spencer +4
While multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by a…