Machine learning-assisted surrogate construction for full-core fuel performance analysis. - Yifeng Che; Joseph Yurko; Koroush Shirvan (CoRR, 2021)

Bayesian Inference with Latent Hamiltonian Neural Networks. - Somayajulu L. N. Dhulipala; Yifeng Che; Michael D. Shields (CoRR, 2022)

Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference. - Somayajulu L. N. Dhulipala; Yifeng Che; Michael D. Shields (CoRR, 2022)

General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation. - Promit Chakroborty; Somayajulu L. N. Dhulipala; Yifeng Che; Wen Jiang 003; Benjamin W. Spencer; Jason D. Hales; Michael D. Shields (CoRR, 2022)

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; Wen Jiang 003; Benjamin W. Spencer; Jason D. Hales; Vincent M. Laboure; Zachary M. Prince; Chandrakanth Bolisetti; Yifeng Che (Reliab. Eng. Syst. Saf., 2022)

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling. - Somayajulu L. N. Dhulipala; Yifeng Che; Michael D. Shields (J. Comput. Phys., 2023)