Poster "neural operators" Papers
16 papers found
Discretization-invariance? On the Discretization Mismatch Errors in Neural Operators
Wenhan Gao, Ruichen Xu, Yuefan Deng et al.
Infinite Neural Operators: Gaussian processes on functions
Daniel Augusto de Souza, Yuchen Zhu, Jake Cunningham et al.
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
Takashi Furuya, Koichi Taniguchi, Satoshi Okuda
S-Crescendo: A Nested Transformer Weaving Framework for Scalable Nonlinear System in S-Domain Representation
Junlang Huang, Chen Hao, Li Luo et al.
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Abdolmehdi Behroozi, Chaopeng Shen, Daniel Kifer
Solving Differential Equations with Constrained Learning
Viggo Moro, Luiz Chamon
Uncertainty-Informed Meta Pseudo Labeling for Surrogate Modeling with Limited Labeled Data
Xingyu Ren, Pengwei Liu, Pengkai Wang et al.
Zero-shot Imputation with Foundation Inference Models for Dynamical Systems
Patrick Seifner, Kostadin Cvejoski, Antonia Körner et al.
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
Levi Lingsch, Mike Yan Michelis, Emmanuel de Bézenac et al.
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
Zhongkai Hao, Chang Su, LIU SONGMING et al.
Neural operators meet conjugate gradients: The FCG-NO method for efficient PDE solving
Alexander Rudikov, Fanaskov Vladimir, Ekaterina Muravleva et al.
Neural Operators with Localized Integral and Differential Kernels
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev et al.
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
Junfeng CHEN, Kailiang Wu
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations
Ze Cheng, Zhongkai Hao, Wang Xiaoqiang et al.
Towards General Neural Surrogate Solvers with Specialized Neural Accelerators
Chenkai Mao, Robert Lupoiu, Tianxiang Dai et al.
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.