Hugging Face Trending Papers August 12, 2026

RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers

Read the original on Hugging Face Trending Papers →

Coarse-grid numerical solvers can substantially reduce the computational cost of time-dependent PDE simulation, but under-resolution often degrades both the trajectory and the spatial fidelity of the solution. We introduce RECAST (Recurrent Error Correction And Super-resolution of coarse-grid Trajectories), a machine-learning framework designed to restore this lost accuracy while retaining coarse-grid evolution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

diffusion

arXiv Machine Learning

Aug 13

RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers

arXiv:2608. 11572v1 Announce Type: new Abstract: Coarse-grid numerical solvers can substantially reduce the computational cost of time-dependent PDE simulation, but under-resolution often degrades both the trajectory and the spatial fidelity of the solution.

By Maryam Reza, Farbod Faraji

diffusion

arXiv Machine Learning

4d ago

Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs. Neural Operators

arXiv:2509.06154v3 Announce Type: replace Abstract: Developing accurate, data-efficient surrogate models is central to advancing AI for Science. Neural operators (NOs), which approximate mappings bet...

By Dibyajyoti Nayak, Somdatta Goswami

benchmarks

arXiv Machine Learning

Sep 1

Learning PDE Time-Stepping with Neural Cellular Automata

arXiv:2608.30328v1 Announce Type: new Abstract: Classical numerical solvers for partial differential equations (PDEs) are computationally expensive to solve repeatedly across varying initial conditio...

By Esha Saha, Hao Wang

benchmarks

arXiv AI

Jul 15

Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance

arXiv:2607. 11974v1 Announce Type: cross Abstract: Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen.

By Zixuan Shen (Central South University), Bingchuan Wang (Central South University), Zhi Wang (Nanjing University), Yong Wang (Central South University)

diffusion

arXiv AI

Jun 30

McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation

arXiv:2606. 30495v1 Announce Type: cross Abstract: Solving heterogeneous Helmholtz equations at high wavenumbers remains challenging because the discretized operator is indefinite, pollution degrades phase accuracy, and scalar coarse-grid correction can discard the local phase and propagation-direction information carried by oscillatory errors.

By Jiwei Jia, Xinliang Liu, Juntao Wang, Jinchao Xu

fine-tuning

arXiv Machine Learning

Jul 16

Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements

arXiv:2601. 20496v2 Announce Type: replace-cross Abstract: Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other applications.

By Ofek Aloni, Barak Fishbain

benchmarks

One email a morning, machine-written

Related stories

RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers

Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs. Neural Operators

Learning PDE Time-Stepping with Neural Cellular Automata

Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance

McMg: A Learned Phase-Space Multi-channel Multigrid Preconditioner for Helmholtz Equation

Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements