Image de couverture de livre

Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Khodakarami, Siavash, Oommen, Vivek, Daryakenari, Nazanin Ahmadi, Beekenkamp, Maxim, Karniadakis, George Em
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
par: Khodakarami, Siavash, et autres
Publié: (2025)

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
par: Oommen, Vivek, et autres
Publié: (2025)

CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics
par: Daryakenari, Nazanin Ahmadi, et autres
Publié: (2024)

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
par: Toscano, Juan Diego, et autres
Publié: (2024)

State-space models are accurate and efficient neural operators for dynamical systems
par: Hu, Zheyuan, et autres
Publié: (2024)

Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
par: Oommen, Vivek, et autres
Publié: (2024)

Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
par: Rubini, Ramona, et autres
Publié: (2025)

Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology
par: Daryakenari, Nazanin Ahmadi, et autres
Publié: (2025)

RiemannONets: Interpretable Neural Operators for Riemann Problems
par: Peyvan, Ahmad, et autres
Publié: (2024)

Physics-Informed Neural Networks and Extensions
par: Raissi, Maziar, et autres
Publié: (2024)

Physics-Informed Machine Learning in Biomedical Science and Engineering
par: Ahmadi, Nazanin, et autres
Publié: (2025)

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
par: Toscano, Juan Diego, et autres
Publié: (2025)

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
par: Jnini, Anas, et autres
Publié: (2026)

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
par: Zou, Zongren, et autres
Publié: (2025)

Spectral Audit of In-Context Operator Networks
par: Gao, Zhiwei, et autres
Publié: (2026)

GMC-PINNs: A new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains
par: Wang, Shupeng, et autres
Publié: (2024)

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology
par: De Florio, Mario, et autres
Publié: (2024)

Agentic Risk-Aware Set-Based Engineering Design
par: Kumar, Varun, et autres
Publié: (2026)

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
par: Gao, Zhiwei, et autres
Publié: (2025)

Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
par: Zhang, Qian, et autres
Publié: (2026)

Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
par: Kumar, Varun, et autres
Publié: (2025)

Connecting the geometry and dynamics of many-body complex systems with message passing neural operators
par: Gabriel, Nicholas A., et autres
Publié: (2025)

Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks
par: Carvalho, Felipe de Castro Teixeira, et autres
Publié: (2023)

Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
par: Li, David S., et autres
Publié: (2025)

DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations
par: Wan, Ruyin, et autres
Publié: (2025)

UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning
par: Qiao, Hanli, et autres
Publié: (2026)

DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks
par: Jiang, Qile, et autres
Publié: (2025)

Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
par: Peyvan, Ahmad, et autres
Publié: (2025)

NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
par: Levrero-Florencio, Francesc, et autres
Publié: (2026)

Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations
par: Song, Jin, et autres
Publié: (2024)

Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
par: Kopaničáková, Alena, et autres
Publié: (2023)

Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
par: Chen, Paula, et autres
Publié: (2023)

SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
par: Varghese, Alan John, et autres
Publié: (2024)

Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics
par: Pandey, Additi, et autres
Publié: (2025)

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
par: Shukla, Khemraj, et autres
Publié: (2024)

GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
par: Toscano, Juan Diego, et autres
Publié: (2026)

Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks
par: Toscano, Juan Diego, et autres
Publié: (2024)

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications
par: Lee, Youngkyu, et autres
Publié: (2024)

L-HYDRA: Multi-Head Physics-Informed Neural Networks
par: Zou, Zongren, et autres
Publié: (2023)

Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
par: Michałowska, Katarzyna, et autres
Publié: (2023)