Showing 1–18 of 18 results
/ Date/ Name
Oct 8, 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorsJul 31, 2021Optimal unit triangular factorization of symplectic matricesFeb 12, 2022MIONet: Learning multiple-input operators via tensor productJan 11, 2020SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systemsMay 27, 2019Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothnessFeb 11, 2024A hybrid iterative method based on MIONet for PDEs: Theory and numerical examplesFeb 23, 2024Learning solution operators of PDEs defined on varying domains via MIONetDec 23, 2025Manifold Function Encoder: Identifying Different Functions Defined on Different ManifoldsDec 2, 2024A deformation-based framework for learning solution mappings of PDEs defined on varying domainsDec 5, 2020Learning Poisson systems and trajectories of autonomous systems via Poisson neural networksDec 23, 2019Unit triangular factorization of the matrix symplectic groupFeb 1, 2023Experimental observation on a low-rank tensor model for eigenvalue problemsApr 23, 2020Deep Hamiltonian networks based on symplectic integratorsJun 21, 2021Approximation capabilities of measure-preserving neural networksSep 2, 2020Inverse modified differential equations for discovery of dynamicsMar 9, 2024Two-hidden-layer ReLU neural networks and finite elementsJul 6, 2022Tensor Neural Network and Its Numerical IntegrationJun 15, 2022On Numerical Integration in Neural Ordinary Differential Equations