Similar Items
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)
by: Zhang, Benjamin J., et al.
Published: (2025)
Latent representation learning based model correction and uncertainty quantification for PDEs
by: Zhou, Wenwen, et al.
Published: (2026)
by: Zhou, Wenwen, et al.
Published: (2026)
Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
by: Zanoni, Andrea, et al.
Published: (2024)
by: Zanoni, Andrea, et al.
Published: (2024)
LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
by: Katz, Sarah, et al.
Published: (2026)
by: Katz, Sarah, et al.
Published: (2026)
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
by: Chen, Wei, et al.
Published: (2025)
by: Chen, Wei, et al.
Published: (2025)
Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems
by: Wang, Xintong, et al.
Published: (2025)
by: Wang, Xintong, et al.
Published: (2025)
Adaptive operator learning for infinite-dimensional Bayesian inverse problems
by: Gao, Zhiwei, et al.
Published: (2023)
by: Gao, Zhiwei, et al.
Published: (2023)
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
by: Xu, Tengfei, et al.
Published: (2023)
by: Xu, Tengfei, et al.
Published: (2023)
Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations
by: Akande, Oluwatosin, et al.
Published: (2025)
by: Akande, Oluwatosin, et al.
Published: (2025)
Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation
by: Zhou, Hanfei, et al.
Published: (2025)
by: Zhou, Hanfei, et al.
Published: (2025)
Nonlinear model reduction for operator learning
by: Eivazi, Hamidreza, et al.
Published: (2024)
by: Eivazi, Hamidreza, et al.
Published: (2024)
Mitigating spectral bias for the multiscale operator learning
by: Liu, Xinliang, et al.
Published: (2022)
by: Liu, Xinliang, et al.
Published: (2022)
Learning cardiac activation and repolarization times with operator learning
by: Centofanti, Edoardo, et al.
Published: (2025)
by: Centofanti, Edoardo, et al.
Published: (2025)
Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
by: Qiu, Yuan, et al.
Published: (2025)
by: Qiu, Yuan, et al.
Published: (2025)
Optimal deep learning of holomorphic operators between Banach spaces
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)
by: Ma, Qinglong, et al.
Published: (2024)
Orthogonal greedy algorithm for linear operator learning with shallow neural network
by: Lin, Ye, et al.
Published: (2025)
by: Lin, Ye, et al.
Published: (2025)
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
by: Luo, Dingcheng, et al.
Published: (2025)
by: Luo, Dingcheng, et al.
Published: (2025)
An Efficient Deep Learning Approach for Approximating Parameter-to-Solution Maps of PDEs
by: Lei, Guanhang, et al.
Published: (2024)
by: Lei, Guanhang, et al.
Published: (2024)
Dilated convolution neural operator for multiscale partial differential equations
by: Xu, Bo, et al.
Published: (2024)
by: Xu, Bo, et al.
Published: (2024)
Deciphering and integrating invariants for neural operator learning with various physical mechanisms
by: Zhang, Rui, et al.
Published: (2023)
by: Zhang, Rui, et al.
Published: (2023)
Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)
by: Beck, Christian, et al.
Published: (2020)
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
by: Morrison, Oisín M., et al.
Published: (2024)
by: Morrison, Oisín M., et al.
Published: (2024)
FLUID: Flow-based Unified Inference for Dynamics
by: Cui, Tiangang, et al.
Published: (2026)
by: Cui, Tiangang, et al.
Published: (2026)
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
by: Hong, Youngjoon, et al.
Published: (2024)
by: Hong, Youngjoon, et al.
Published: (2024)
Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
by: Liu, Ye, et al.
Published: (2024)
by: Liu, Ye, et al.
Published: (2024)
A real-time battle situation intelligent awareness system based on Meta-learning & RNN
by: Li, Yuchun, et al.
Published: (2025)
by: Li, Yuchun, et al.
Published: (2025)
NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
by: Levrero-Florencio, Francesc, et al.
Published: (2026)
by: Levrero-Florencio, Francesc, et al.
Published: (2026)
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
by: Guo, Yuan, et al.
Published: (2025)
by: Guo, Yuan, et al.
Published: (2025)
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
by: Jentzen, Arnulf, et al.
Published: (2023)
by: Jentzen, Arnulf, et al.
Published: (2023)
Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
by: Zhou, Hanfei, et al.
Published: (2025)
by: Zhou, Hanfei, et al.
Published: (2025)
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
by: Williams, Emily, et al.
Published: (2024)
by: Williams, Emily, et al.
Published: (2024)
Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
by: Vitullo, Piermario, et al.
Published: (2024)
by: Vitullo, Piermario, et al.
Published: (2024)
Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
by: Thanasutives, Pongpisit, et al.
Published: (2024)
by: Thanasutives, Pongpisit, et al.
Published: (2024)
Lattice-based Deep Neural Networks: Regularity and Tailored Regularization
by: Keller, Alexander, et al.
Published: (2026)
by: Keller, Alexander, et al.
Published: (2026)
Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins
by: Ning, Zhiyang, et al.
Published: (2025)
by: Ning, Zhiyang, et al.
Published: (2025)
A deformation-based framework for learning solution mappings of PDEs defined on varying domains
by: Xiao, Shanshan, et al.
Published: (2024)
by: Xiao, Shanshan, et al.
Published: (2024)
A Generative Sampler for distributions with possible discrete parameter based on Reversibility
by: Li, Lei, et al.
Published: (2026)
by: Li, Lei, et al.
Published: (2026)
Physics-based machine learning for fatigue lifetime prediction under non-uniform loading scenarios
by: Baktheer, Abedulgader, et al.
Published: (2025)
by: Baktheer, Abedulgader, et al.
Published: (2025)
Generating synthetic data for neural operators
by: Hasani, Erisa, et al.
Published: (2024)
by: Hasani, Erisa, et al.
Published: (2024)
Similar Items
-
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025) -
Latent representation learning based model correction and uncertainty quantification for PDEs
by: Zhou, Wenwen, et al.
Published: (2026) -
Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
by: Zanoni, Andrea, et al.
Published: (2024) -
LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
by: Katz, Sarah, et al.
Published: (2026) -
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
by: Chen, Wei, et al.
Published: (2025)