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Experiment-informed finite-strain inverse design of spinodal metamaterials.

P. Thakolkaran, M. Espinal, S. Dhulipala, S. Kumar, and C. Portela. CoRR, (2023)

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Experiment-informed finite-strain inverse design of spinodal metamaterials.P. Thakolkaran, M. Espinal, S. Dhulipala, S. Kumar, and C. Portela. CoRR, (2023)MOOSE Stochastic Tools: A module for performing parallel, memory-efficient in situ stochastic simulations.A. Slaughter, Z. Prince, P. German, I. Halvic, W. Jiang, B. Spencer, S. Dhulipala, and D. Gaston. SoftwareX, (May 2023)Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation.S. Dhulipala, M. Shields, P. Chakroborty, W. Jiang, B. Spencer, J. Hales, V. Laboure, Z. Prince, C. Bolisetti, and Y. Che. Reliab. Eng. Syst. Saf., (2022)Bayesian Inference with Latent Hamiltonian Neural Networks.S. Dhulipala, Y. Che, and M. Shields. CoRR, (2022)Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks.D. Thaler, S. Dhulipala, F. Bamer, B. Markert, and M. Shields. CoRR, (2024)Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference.S. Dhulipala, Y. Che, and M. Shields. CoRR, (2022)Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling.S. Dhulipala, Y. Che, and M. Shields. J. Comput. Phys., (November 2023)Series of semi-Markov processes to model infrastructure resilience under multihazards.S. Dhulipala, and M. Flint. Reliab. Eng. Syst. Saf., (2020)Active learning with multifidelity modeling for efficient rare event simulation.S. Dhulipala, M. Shields, B. Spencer, C. Bolisetti, A. Slaughter, V. Laboure, and P. Chakroborty. J. Comput. Phys., (2022)Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation.S. Dhulipala, M. Shields, B. Spencer, C. Bolisetti, A. Slaughter, V. Laboure, and P. Chakroborty. CoRR, (2021)

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Experiment-informed finite-strain inverse design of spinodal metamaterials.

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Disambiguation of "Dhulipala, Somayajulu"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Experiment-informed finite-strain inverse design of spinodal metamaterials.

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Experiment-informed finite-strain inverse design of spinodal metamaterials.