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Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.

, , , , and . CoRR, (2024)

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ULFM-MPI Implementation of a Resilient Task-Based Partial Differential Equations Preconditioner., , , , , , , and . FTXS@HPDC, page 19-26. ACM, (2016)TChem: A performance portable parallel software toolkit for complex kinetic mechanisms., , , , and . Comput. Phys. Commun., (April 2023)Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone., , , , , , and . CoRR, (2024)Uncertainty Quantification in the Presence of Limited Climate Model Data with Discontinuities., , , and . ICDM Workshops, page 241-247. IEEE Computer Society, (2009)A second-order coupled immersed boundary-SAMR construction for chemically reacting flow over a heat-conducting Cartesian grid-conforming solid., , , , and . J. Comput. Phys., (2014)Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models., , , , and . CoRR, (2024)Partial Differential Equations Preconditioner Resilient to Soft and Hard Faults., , , , , , , and . CLUSTER, page 552-562. IEEE Computer Society, (2015)Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response., , and . CoRR, (2024)A high-order low-Mach number AMR construction for chemically reacting flows., , and . J. Comput. Phys., 229 (24): 9299-9322 (2010)Scalability of Partial Differential Equations Preconditioner Resilient to Soft and Hard Faults., , , , , , , , and . ISC, volume 9697 of Lecture Notes in Computer Science, page 469-485. Springer, (2016)