2 citations · 3 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…
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…
Quantifying the Structure of Disordered Materials
Thomas J. Hardin, Michael Chandross, Rahul Meena +5
Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability…
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…