Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
Fuente:
arXiv
Saved in:
| Main Authors: | Khodakarami, Siavash, Oommen, Vivek, Bora, Aniruddha, Karniadakis, George Em |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
by: Oommen, Vivek, et al.
Published: (2025)
by: Oommen, Vivek, et al.
Published: (2025)
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
by: Oommen, Vivek, et al.
Published: (2024)
by: Oommen, Vivek, et al.
Published: (2024)
Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
by: Khodakarami, Siavash, et al.
Published: (2026)
by: Khodakarami, Siavash, et al.
Published: (2026)
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
by: Rubini, Ramona, et al.
Published: (2025)
by: Rubini, Ramona, et al.
Published: (2025)
A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
by: Toscano, Juan Diego, et al.
Published: (2025)
by: Toscano, Juan Diego, et al.
Published: (2025)
RiemannONets: Interpretable Neural Operators for Riemann Problems
by: Peyvan, Ahmad, et al.
Published: (2024)
by: Peyvan, Ahmad, et al.
Published: (2024)
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
by: Bora, Aniruddha, et al.
Published: (2025)
by: Bora, Aniruddha, et al.
Published: (2025)
Equilibrium Conserving Neural Operators for Super-Resolution Learning
by: Oommen, Vivek, et al.
Published: (2025)
by: Oommen, Vivek, et al.
Published: (2025)
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
by: Toscano, Juan Diego, et al.
Published: (2024)
by: Toscano, Juan Diego, et al.
Published: (2024)
SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
by: Varghese, Alan John, et al.
Published: (2024)
by: Varghese, Alan John, et al.
Published: (2024)
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
by: Zou, Zongren, et al.
Published: (2025)
by: Zou, Zongren, et al.
Published: (2025)
L-HYDRA: Multi-Head Physics-Informed Neural Networks
by: Zou, Zongren, et al.
Published: (2023)
by: Zou, Zongren, et al.
Published: (2023)
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
by: Zhang, Qian, et al.
Published: (2026)
by: Zhang, Qian, et al.
Published: (2026)
Spectral Audit of In-Context Operator Networks
by: Gao, Zhiwei, et al.
Published: (2026)
by: Gao, Zhiwei, et al.
Published: (2026)
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
by: Michałowska, Katarzyna, et al.
Published: (2023)
by: Michałowska, Katarzyna, et al.
Published: (2023)
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
by: Wei, Jiawen, et al.
Published: (2025)
by: Wei, Jiawen, et al.
Published: (2025)
Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
by: Shukla, Khemraj, et al.
Published: (2026)
by: Shukla, Khemraj, et al.
Published: (2026)
Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
by: Peyvan, Ahmad, et al.
Published: (2025)
by: Peyvan, Ahmad, et al.
Published: (2025)
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
by: Shukla, Khemraj, et al.
Published: (2024)
by: Shukla, Khemraj, et al.
Published: (2024)
Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
by: Gao, Zhiwei, et al.
Published: (2025)
by: Gao, Zhiwei, et al.
Published: (2025)
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
by: Toscano, Juan Diego, et al.
Published: (2025)
by: Toscano, Juan Diego, et al.
Published: (2025)
UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning
by: Qiao, Hanli, et al.
Published: (2026)
by: Qiao, Hanli, et al.
Published: (2026)
Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
by: Wen, Haizhou, et al.
Published: (2026)
by: Wen, Haizhou, et al.
Published: (2026)
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
by: Shih, Benjamin, et al.
Published: (2024)
by: Shih, Benjamin, et al.
Published: (2024)
Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
by: Kim, Heechang, et al.
Published: (2026)
by: Kim, Heechang, et al.
Published: (2026)
A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoring
by: Nath, Kamaljyoti, et al.
Published: (2024)
by: Nath, Kamaljyoti, et al.
Published: (2024)
Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)
by: Raissi, Maziar, et al.
Published: (2024)
GMC-PINNs: A new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains
by: Wang, Shupeng, et al.
Published: (2024)
by: Wang, Shupeng, et al.
Published: (2024)
Connecting the geometry and dynamics of many-body complex systems with message passing neural operators
by: Gabriel, Nicholas A., et al.
Published: (2025)
by: Gabriel, Nicholas A., et al.
Published: (2025)
Two-scale Neural Networks for Partial Differential Equations with Small Parameters
by: Zhuang, Qiao, et al.
Published: (2024)
by: Zhuang, Qiao, et al.
Published: (2024)
Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations
by: Song, Jin, et al.
Published: (2024)
by: Song, Jin, et al.
Published: (2024)
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks
by: Hu, Zheyuan, et al.
Published: (2023)
by: Hu, Zheyuan, et al.
Published: (2023)
Agentic Risk-Aware Set-Based Engineering Design
by: Kumar, Varun, et al.
Published: (2026)
by: Kumar, Varun, et al.
Published: (2026)
Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation
by: Bora, Aniruddha, et al.
Published: (2026)
by: Bora, Aniruddha, et al.
Published: (2026)
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks
by: Toscano, Juan Diego, et al.
Published: (2024)
by: Toscano, Juan Diego, et al.
Published: (2024)
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
by: Lee, Youngkyu, et al.
Published: (2024)
by: Lee, Youngkyu, et al.
Published: (2024)
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations
by: Hu, Zheyuan, et al.
Published: (2024)
by: Hu, Zheyuan, et al.
Published: (2024)
Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
by: Kumar, Varun, et al.
Published: (2025)
by: Kumar, Varun, et al.
Published: (2025)
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models
by: Meng, Tingwei, et al.
Published: (2024)
by: Meng, Tingwei, et al.
Published: (2024)
Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
by: Li, David S., et al.
Published: (2025)
by: Li, David S., et al.
Published: (2025)
Similar Items
-
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
by: Oommen, Vivek, et al.
Published: (2025) -
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
by: Oommen, Vivek, et al.
Published: (2024) -
Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
by: Khodakarami, Siavash, et al.
Published: (2026) -
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
by: Rubini, Ramona, et al.
Published: (2025) -
A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
by: Toscano, Juan Diego, et al.
Published: (2025)