b'PUBLICATIONS AND PRESENTATIONS: S.L.N. Dhulipala, P. Chakroborty, M.D. Shields, W. Jiang, B.W. S.L.N. Dhulipala, W. Jiang, B.W. Spencer, J.D. Hales, M.D.Spencer, J.D. Hales, Multifidelity Active Learning for Failure Shields, A.E. Slaughter, Z.M. Prince, V.M. Labour, C. Bolisetti,Estimation of TRISO Nuclear Fuel, 37th AAAI Conference on P. Chakroborty, Accelerated statistical failure analysis ofArtificial Intelligence.multifidelity TRISO fuel models, Journal of Nuclear Materials,D. Thaler, S.L.N. Dhulipala, B. Markert, F. Bamer, M.D. 563 (2022) 153604. Shields, Enhanced Hamiltonian Monte Carlo simulations S.L.N. Dhulipala, M.D. Shields, P. Chakroborty, W. Jiang,using Hamiltonian neural networks, 92nd Annual Meeting Benjamin W. Spencer, J.D. Hales, V. M. Labour, Z.M. Prince, C.of the International Association of Applied Mathematics Bolisetti, Y. Che, Reliability estimation of an advanced nuclearand Mechanics (published in the Proceedings in Applied fuel using coupled active learning, multifidly modeling, andMathematics & Mechanics).subset simulation, Reliability Engineering & System Safety, 226D. Thaler, S.L.N. Dhulipala, B. Markert, F. Bamer, M.D. Shields, (2022) 108693. Efficient Subset Simulation using latent Hamiltonian Neural S.L.N. Dhulipala, M.D. Shields, B.W. Spencer, C. Bolisetti, A.E.Network enhanced Markov-Chain Monte Carlo methods, Slaughter, V.M. Labour, P. Chakroborty, Active Learning withInternational Conference on Applications of StatisticsMultifidelity Modeling for Efficient Rare Event Simulation,and Probability.Journal of Computational Physics, 468 (2022) 111506. P. Chakroborty, M.D. Shields, S.L.N. Dhulipala, Reliability P. Chakroborty, S.L.N. Dhulipala, Y. Che, W. Jiang, B.W. Spencer,Analysis using Multiple Low-fidelity Models Coupled J.D. Hales, M.D. Shields, General Multifidelity Surrogatewith Active Learning: A Robust, General, and Explainable Models: Framework and Active Learning Strategies for EfficientFramework, International Conference on Applications of Rare Event Simulation, Journal of Engineering Mechanics,Statistics and Probability.volume 149, number 12 (2023).S.L.N. Dhulipala, Y. Che, M.D. Shields, Efficient BayesianINTELLECTUAL PROPERTY:Inference with Latent Hamiltonian Neural Networks in No-U- BIhNNs: Bayesian Inference with Neural Networks Github link: Turn Sampling, Journal of Computational Physics, 492 (2023)https://github.com/IdahoLabResearch/BIhNNs. 112425. AWARDS:S.L.N. Dhulipala, Z.M. Prince, A. Slaughter, L.B. Munday, W.Som Dhulipala: Conference Award, 16th US National Congress Jiang, B.W. Spencer, J.D. Hales, Monte Carlo Variance Reductionon Computational Mechanics.in MOOSE Stochastic Tools Module: Accelerating the FailurePromit Chakroborty: Outstanding Contribution Prize, Workshop Analysis of Nuclear Reactor Technologies, Internationalon Measuring the quality of MCMC output, International Society Conference on Physics of Reactors. for Bayesian Analysis.S.L.N. Dhulipala, Y. Che, M.D. Shields, Physics-InformedEusef Abdelmalek: Graduate Education for Minorities Machine Learning of Dynamical Systems for Efficient BayesianConsortium Fellowship.Inference, NeurIPS Workshop on Machine Learning and the Physical Sciences.44'