:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	He, Andrew Qing, Cai, Wei
Format:	Preprint
Published:	2026
Subjects:	Numerical Analysis Analysis of PDEs 65N75, 68T07, 35R11, 60G52
Online Access:	https://arxiv.org/abs/2603.12869
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Weak Adversarial Neural Pushforward Method for the McKean-Vlasov / Mean-Field Fokker-Planck Equation
by: He, Andrew Qing, et al.
Published: (2026)

Learning Neural Pushforward Samplers for Distributions from Fokker-Planck Equations by Weak Adversarial Training
by: He, Andrew Qing, et al.
Published: (2025)

Neural Pushforward Samplers for the Fokker-Planck Equation on Embedded Riemannian Manifolds
by: He, Andrew Qing, et al.
Published: (2026)

Weak Adversarial Neural Pushforward Method for the Wigner Transport Equation
by: He, Andrew Qing, et al.
Published: (2026)

Transport based particle methods for the Fokker-Planck-Landau equation
by: Ilin, Vasily, et al.
Published: (2024)

The lowest-order Neural Approximated Virtual Element Method on polygonal elements
by: Berrone, Stefano, et al.
Published: (2024)

Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators
by: Cao, Xiang, et al.
Published: (2025)

Solving Inverse PDE Problems using Minimization Methods and AI
by: Helwani, Noura Al, et al.
Published: (2026)

Extension and neural operator approximation of the electrical impedance tomography inverse map
by: de Hoop, Maarten V., et al.
Published: (2025)

ARDO: A Weak Formulation Deep Neural Network Method for Elliptic and Parabolic PDEs Based on Random Differences of Test Functions
by: Cai, Wei, et al.
Published: (2025)

Deep-Picard Iteration for Space-time Fractional Diffusion PDEs
by: Zeng, Zhijun, et al.
Published: (2026)

Adaptive Probability Flow Residual Minimization for High-Dimensional Fokker-Planck Equations
by: Wu, Xiaolong, et al.
Published: (2025)

Regularity and tailored regularization of Deep Neural Networks, with application to parametric PDEs in uncertainty quantification
by: Keller, Alexander, et al.
Published: (2025)

Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian
by: Bungert, Leon, et al.
Published: (2022)

Data Completion for Electrical Impedance Tomography by Conditional Diffusion Models
by: Chen, Ke, et al.
Published: (2026)

Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
by: Alberti, Giovanni S., et al.
Published: (2026)

FieldTNN-based machine learning method for Maxwell eigenvalue problems
by: Jiang, Jiantao, et al.
Published: (2024)

Finite Element Approximation of the Fractional Porous Medium Equation
by: Carrillo, José A., et al.
Published: (2024)

A minimax method for the spectral fractional Laplacian and related evolution problems
by: Carrillo, José A., et al.
Published: (2025)

Qualitative reconstruction methods for imaging interior Robin interfaces in EIT from Robin-to-Dirichlet data
by: Ayala, Rafael Ceja, et al.
Published: (2026)

Inverse Electromagnetic Scattering for Doubly-Connected Cylinders using Convolutional Neural Networks
by: Mindrinos, Leonidas, et al.
Published: (2025)

The learned range test method for the inverse inclusion problem
by: Sun, Shiwei, et al.
Published: (2024)

Neural Network Localized Orthogonal Decomposition for Numerical Homogenization of Diffusion Operators with Random Coefficients
by: Kröpfl, Fabian, et al.
Published: (2025)

Neural enrichment finite element method: A hybrid framework for problems with strong oscillations or interface problems
by: Guo, Shihan, et al.
Published: (2026)

Least Squares with Equality constraints Extreme Learning Machines for the resolution of PDEs
by: De Falco, Davide Elia, et al.
Published: (2025)

A convergent scheme for the Bayesian filtering problem based on the Fokker--Planck equation and deep splitting
by: Bågmark, Kasper, et al.
Published: (2024)

Error Analysis of Three-Layer Neural Network Trained with PGD for Deep Ritz Method
by: Jiao, Yuling, et al.
Published: (2024)

Machine learning of discrete field theories with guaranteed convergence and uncertainty quantification
by: Offen, Christian
Published: (2024)

Quantitative analysis for $L^2$-estimates in linear elliptic equations via divergence-free transformation
by: Lee, Haesung
Published: (2025)

A Carleman Semi-Discrete Convexification Method Combined With Deep Learning for Electrical Impedance Tomography
by: Klibanov, Michael V., et al.
Published: (2026)

Random domain decomposition for parabolic PDEs on graphs
by: Hernández, Martín
Published: (2025)

Self-adaptive physics-informed neural network for forward and inverse problems in heterogeneous porous flow
by: Aziz, Md. Abdul, et al.
Published: (2025)

Stochastic Galerkin methods for linear stability analysis of systems with parametric uncertainty
by: Sousedík, Bedřich, et al.
Published: (2022)

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
by: Codega, Gabriele, et al.
Published: (2025)

Expression Rates of Neural Operators for Linear Elliptic PDEs in Polytopes
by: Marcati, Carlo, et al.
Published: (2024)

Machine learning on manifolds for inverse scattering: Lipschitz stability analysis
by: Ganesh, Mahadevan, et al.
Published: (2025)

Numerical solution of a PDE arising from prediction with expert advice
by: Calder, Jeff, et al.
Published: (2024)

Data-Free Asymptotics-Informed Operator Networks for Singularly Perturbed PDEs
by: Lee, Jinsil, et al.
Published: (2025)

A Neural Multigrid Solver for Helmholtz Equations with High Wavenumber and Heterogeneous Media
by: Cui, Chen, et al.
Published: (2024)

An explicit Euler method for Sobolev vector fields with applications to the continuity equation on non cartesian grids
by: Cortopassi, Tommaso
Published: (2024)