:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Liu, Lei, Huang, Zhenxin, Wang, Hong, dong, huanshuo, Xin, Haiyang, Zhao, Hongwei, Li, Bin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2510.21592
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter
by: Wang, Hong, et al.
Published: (2025)

From Uniform to Adaptive: General Skip-Block Mechanisms for Efficient PDE Neural Operators
by: Liu, Lei, et al.
Published: (2025)

TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalization
by: Liu, Lei, et al.
Published: (2026)

Accelerating PDE Data Generation via Differential Operator Action in Solution Space
by: Dong, Huanshuo, et al.
Published: (2024)

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
by: Wang, Hong, et al.
Published: (2024)

Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
by: Dang, Zhenhua, et al.
Published: (2026)

Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs
by: Yuan, Qiwei, et al.
Published: (2025)

Toward Efficient Kernel-Based Solvers for Nonlinear PDEs
by: Xu, Zhitong, et al.
Published: (2024)

Phys-Diff: A Physics-Inspired Latent Diffusion Model for Tropical Cyclone Forecasting
by: Liu, Lei, et al.
Published: (2026)

Learning solution operators of PDEs defined on varying domains via MIONet
by: Xiao, Shanshan, et al.
Published: (2024)

Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
by: Taniguchi, Koichi, et al.
Published: (2026)

Robust Moment Identification for Nonlinear PDEs via a Neural ODE Approach
by: Chen, Shaoxuan, et al.
Published: (2025)

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
by: Li, Zongyi, et al.
Published: (2022)

Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift
by: Zhao, Haiyang
Published: (2026)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
by: Baptista, Ricardo, et al.
Published: (2025)

Data-Driven Discovery of PDEs via the Adjoint Method
by: Sadr, Mohsen, et al.
Published: (2024)

Efficient Data Distribution Estimation for Accelerated Federated Learning
by: Wang, Yuanli, et al.
Published: (2024)

One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
by: Auroy, Samuel, et al.
Published: (2025)

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
by: Shao, Zihan, et al.
Published: (2025)

Data Driven Decision Making with Time Series and Spatio-temporal Data
by: Yang, Bin, et al.
Published: (2025)

Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
by: Baba, Zakaria, et al.
Published: (2026)

Generalized Low-Rank Matrix Contextual Bandits with Graph Information
by: Wang, Yao, et al.
Published: (2025)

The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction
by: Li, Beibei
Published: (2025)

Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations
by: Cheng, Ze, et al.
Published: (2024)

Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport
by: Hong, Haokai, et al.
Published: (2024)

An Efficient Deep Learning Approach for Approximating Parameter-to-Solution Maps of PDEs
by: Lei, Guanhang, et al.
Published: (2024)

The Rashomon Effect for Visualizing High-Dimensional Data
by: Sun, Yiyang, et al.
Published: (2026)

Compositional Generation for Long-Horizon Coupled PDEs
by: Dhulipala, Somayajulu L. N., et al.
Published: (2025)

Extrapolability Improvement of Machine Learning-Based Evapotranspiration Models via Domain-Adversarial Neural Networks
by: Shi, Haiyang
Published: (2024)

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data
by: Xin, Lei, et al.
Published: (2025)

Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
by: Yang, Zherui, et al.
Published: (2026)

Advancing Counterfactual Inference through Nonlinear Quantile Regression
by: Xie, Shaoan, et al.
Published: (2023)

Neural-Initialized Newton: Accelerating Nonlinear Finite Elements via Operator Learning
by: Taghikhani, Kianoosh, et al.
Published: (2025)

Efficient Generative Transformer Operators For Million-Point PDEs
by: Koupaï, Armand Kassaï, et al.
Published: (2025)

Zebra: In-Context Generative Pretraining for Solving Parametric PDEs
by: Serrano, Louis, et al.
Published: (2024)

Exploration via linearly perturbed loss minimisation
by: Janz, David, et al.
Published: (2023)

Accelerated Bayesian Optimal Experimental Design via Conditional Density Estimation and Informative Data
by: Huang, Miao, et al.
Published: (2025)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
by: Lei, Guanhang, et al.
Published: (2023)

Rethinking Diffusion Model in High Dimension
by: Zheng, Zhenxin, et al.
Published: (2025)

Gaussian Process Regression for Inverse Problems in Linear PDEs
by: Li, Xin, et al.
Published: (2025)