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Error-in-variables modelling for operator learning.

, , , and . MSML, volume 190 of Proceedings of Machine Learning Research, page 142-157. PMLR, (2022)

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Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes., , , , and . CoRR, (2024)GMLS-Nets: A Machine Learning Framework for Unstructured Data., , , and . AAAI Spring Symposium: MLPS, volume 2587 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)Error-in-variables modelling for operator learning., , , and . MSML, volume 190 of Proceedings of Machine Learning Research, page 142-157. PMLR, (2022)Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response., , and . CoRR, (2024)A physics-informed operator regression framework for extracting data-driven continuum models., , , and . CoRR, (2020)Thermodynamically consistent physics-informed neural networks for hyperbolic systems., , , , , , and . CoRR, (2020)Partition of Unity Networks: Deep HP-Approximation., , , , and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis Viewpoint., , , , and . MSML, volume 107 of Proceedings of Machine Learning Research, page 512-536. PMLR, (2020)Nonlinear integro-differential operator regression with neural networks., and . CoRR, (2018)Thermodynamically consistent physics-informed neural networks for hyperbolic systems., , , , , , and . J. Comput. Phys., (2022)