:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Oommen, Vivek, Robertson, Andreas E., Diaz, Daniel, Alleman, Coleman, Zhang, Zhen, Rollett, Anthony D., Karniadakis, George E., Dingreville, Rémi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Physics
Online Access:	https://arxiv.org/abs/2504.13422
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
by: Oommen, Vivek, et al.
Published: (2024)

RiemannONets: Interpretable Neural Operators for Riemann Problems
by: Peyvan, Ahmad, et al.
Published: (2024)

Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
by: Khodakarami, Siavash, 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)

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
by: Oommen, Vivek, et al.
Published: (2025)

Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
by: Khodakarami, Siavash, et al.
Published: (2026)

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

Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins
by: Robertson, Andreas E., et al.
Published: (2025)

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
by: Toscano, Juan Diego, et al.
Published: (2024)

Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
by: Michałowska, Katarzyna, et al.
Published: (2023)

UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning
by: Qiao, Hanli, 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)

Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
by: Zhang, Qian, 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)

Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
by: Shih, Benjamin, 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)

XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
by: Wei, Jiawen, et al.
Published: (2025)

Spectral Audit of In-Context Operator Networks
by: Gao, Zhiwei, et al.
Published: (2026)

Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method
by: Kopaničáková, Alena, et al.
Published: (2025)

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
by: Kim, Heechang, et al.
Published: (2026)

A Neural-Operator Preconditioned Newton Method for Accelerated Nonlinear Solvers
by: Lee, Youngkyu, et al.
Published: (2025)

Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider
by: Shukla, Khemraj, et al.
Published: (2024)

AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning
by: Jiang, Qile, et al.
Published: (2025)

Learning interpretable surface elasticity properties from bulk properties
by: Desai, Saaketh, et al.
Published: (2025)

Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
by: Zhang, Enrui, et al.
Published: (2022)

CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics
by: Daryakenari, Nazanin Ahmadi, et al.
Published: (2024)

Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
by: Bora, Aniruddha, et al.
Published: (2025)

Is Zero-Shot Super-Resolution Possible in Operator Learning?
by: Subedi, Unique, et al.
Published: (2026)

L-HYDRA: Multi-Head Physics-Informed Neural Networks
by: Zou, Zongren, et al.
Published: (2023)

Proposal-Guided Greedy Surrogate Refinement for PDE-Driven High-Dimensional Rare-Event Estimation
by: Gao, Zhiwei, et al.
Published: (2026)

SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
by: Abid, Muhammad, et al.
Published: (2026)

Task structure and nonlinearity jointly determine learned representational geometry
by: Alleman, Matteo, et al.
Published: (2024)

Physics-Informed Neural Networks and Extensions
by: Raissi, Maziar, et al.
Published: (2024)

Self-driving lab discovers principles for steering spontaneous emission
by: Desai, Saaketh, 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)

AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
by: Nath, Kamaljyoti, et al.
Published: (2025)

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks
by: Gao, Zhiwei, et al.
Published: (2025)

Agentic Risk-Aware Set-Based Engineering Design
by: Kumar, Varun, et al.
Published: (2026)

Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
by: Kiyani, Elham, et al.
Published: (2025)

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
by: Zou, Zongren, et al.
Published: (2025)