Copertina

Spectral Informed Neural Network: An Efficient and Low-Memory PINN

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yu, Tianchi, Qi, Yiming, Oseledets, Ivan, Chen, Shiyi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Numerical Analysis Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Quasi-Random Physics-informed Neural Networks
di: Yu, Tianchi, et al.
Pubblicazione: (2025)

Astral: training physics-informed neural networks with error majorants
di: Fanaskov, Vladimir, et al.
Pubblicazione: (2024)

Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities
di: Yu, Tianchi, et al.
Pubblicazione: (2024)

Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations
di: Trifonov, Vladislav, et al.
Pubblicazione: (2025)

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
di: Kong, Chun-Wei, et al.
Pubblicazione: (2024)

Data-Efficient Neural Operator Training via Physics-Based Active Learning
di: Polanska, Alicja, et al.
Pubblicazione: (2026)

Hybrid Neural World Models
di: Lakshmanan, Pranav, et al.
Pubblicazione: (2026)

Learning from Integral Losses in Physics Informed Neural Networks
di: Saleh, Ehsan, et al.
Pubblicazione: (2023)

Spectral Analysis of the Weighted Frobenius Objective
di: Trifonov, Vladislav, et al.
Pubblicazione: (2025)

LieSolver: A PDE-constrained solver for IBVPs using Lie symmetries
di: Klausen, René P., et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
di: Mustajab, Abdul Hannan, et al.
Pubblicazione: (2024)

Generative Neural Reparameterization for Differentiable PDE-constrained Optimization
di: Joglekar, Archis S.
Pubblicazione: (2024)

STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
di: Wang, Hong, et al.
Pubblicazione: (2025)

Graph Neural Network as Computationally Efficient Emulator of Ice-sheet and Sea-level System Model (ISSM)
di: Koo, Younghyun, et al.
Pubblicazione: (2024)

Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
di: Sawhney, Medha, et al.
Pubblicazione: (2025)

Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
di: Yang, Yu, et al.
Pubblicazione: (2023)

Breakeven complexity: A new perspective on neural partial differential equation solvers
di: Zhang, Yijing, et al.
Pubblicazione: (2026)

Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
di: Leng, Kuangdai, et al.
Pubblicazione: (2023)

When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
di: Yee, Brandon, et al.
Pubblicazione: (2026)

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
di: Jnini, Anas, et al.
Pubblicazione: (2026)

Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
di: Li, Guoming, et al.
Pubblicazione: (2024)

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
di: Jekic, Aleksandra, et al.
Pubblicazione: (2025)

Quantum Neural Network Restatement of Markov Jump Process
di: Zarezadeh, Z., et al.
Pubblicazione: (2025)

A Dimensionality Reduction Approach for Convolutional Neural Networks
di: Meneghetti, Laura, et al.
Pubblicazione: (2021)

Energy Dissipation Preserving Physics Informed Neural Network for Allen-Cahn Equations
di: Kütük, Mustafa, et al.
Pubblicazione: (2024)

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
di: Rao, Xiang, et al.
Pubblicazione: (2025)

Spectral methods for Neural Integral Equations
di: Zappala, Emanuele
Pubblicazione: (2023)

Learning to Discover Iterative Spectral Algorithms
di: Liu, Zihang, et al.
Pubblicazione: (2026)

Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
di: Della Santa, Francesco, et al.
Pubblicazione: (2026)

Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study
di: Patel, Janak M., et al.
Pubblicazione: (2025)

Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution
di: Yang, Mengxi, et al.
Pubblicazione: (2022)

SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
di: Miñoza, Jose Marie Antonio
Pubblicazione: (2026)

Activations Through Extensions: A Framework To Boost Performance Of Neural Networks
di: Kamanchi, Chandramouli, et al.
Pubblicazione: (2024)

A case study of spatiotemporal forecasting techniques for weather forecasting
di: Sofi, Shakir Showkat, et al.
Pubblicazione: (2022)

Graph Neural Networks for Emulation of Finite-Element Ice Dynamics in Greenland and Antarctic Ice Sheets
di: Koo, Younghyun, et al.
Pubblicazione: (2024)

Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
di: Ke, Yekun, et al.
Pubblicazione: (2024)

Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
di: Qian, Yianxia, et al.
Pubblicazione: (2024)

CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks
di: Dong, Zhikang, et al.
Pubblicazione: (2022)

CodePDE: An Inference Framework for LLM-driven PDE Solver Generation
di: Li, Shanda, et al.
Pubblicazione: (2025)

Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
di: Es'kin, Vasiliy A., et al.
Pubblicazione: (2025)