Cover Image

A neural operator framework for data-driven discovery of stability and receptivity in physical systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Chengyun, Chen, Liwei, Thuerey, Nils
Format:	Preprint
Published:	2026
Subjects:	Fluid Dynamics Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
by: Lino, Mario, et al.
Published: (2025)

One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions
by: Lino, Mario, et al.
Published: (2026)

diffSPH: Differentiable Smoothed Particle Hydrodynamics for Adjoint Optimization and Machine Learning
by: Winchenbach, Rene, et al.
Published: (2025)

ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection
by: Thakur, Sukirt, et al.
Published: (2022)

MetaOpenFOAM: an LLM-based multi-agent framework for CFD
by: Chen, Yuxuan, et al.
Published: (2024)

SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
by: Yang, Yunjia, et al.
Published: (2025)

LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements
by: Hetherington, Ashton, et al.
Published: (2024)

Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
by: Wang, Shaofan, et al.
Published: (2025)

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
by: Kohl, Georg, et al.
Published: (2023)

Deep learning-based predictive modelling of transonic flow over an aerofoil
by: Chen, Li-Wei, et al.
Published: (2024)

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
by: Shan, Siming, et al.
Published: (2024)

Data-driven discovery of drag-inducing elements on a rough surface through convolutional neural networks
by: Shin, Heesoo, et al.
Published: (2024)

Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
by: Liu, Yunfei, et al.
Published: (2026)

Deep reinforcement learning for tracking a moving target in jellyfish-like swimming
by: Chen, Yihao, et al.
Published: (2024)

Implicit factorized transformer approach to fast prediction of turbulent channel flows
by: Yang, Huiyu, et al.
Published: (2024)

Flow reconstruction in time-varying geometries using graph neural networks
by: Danciu, Bogdan A., et al.
Published: (2024)

Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method
by: Wang, Xutun, et al.
Published: (2024)

Spatiotemporal wall pressure forecast of a rectangular cylinder with physics-aware DeepU-Fourier neural network
by: Liu, Junle, et al.
Published: (2025)

Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
by: Chen, Shengyu, et al.
Published: (2024)

LLM Agent for Fire Dynamics Simulations
by: Xu, Leidong, et al.
Published: (2024)

OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD
by: Chen, Yuxuan, et al.
Published: (2025)

Physics-guided surrogate learning enables zero-shot control of turbulent wings
by: Wang, Yuning, et al.
Published: (2026)

Global Transport for Fluid Reconstruction with Learned Self-Supervision
by: Franz, Aleksandra, et al.
Published: (2021)

A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
by: Yalamanchi, Kiran, et al.
Published: (2026)

PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
by: Jiang, Liang, et al.
Published: (2025)

Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling
by: Yang, Sunwoong, et al.
Published: (2025)

A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application
by: Lagemann, Esther, et al.
Published: (2024)

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
by: Zou, Xintong, et al.
Published: (2025)

High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
by: Montalà, Ricard, et al.
Published: (2026)

Data-Free PINNs for Compressible Flows: Mitigating Spectral Bias and Gradient Pathologies via Mach-Guided Scaling and Hybrid Convolutions
by: Yano, Ryosuke
Published: (2026)

AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
by: Somasekharan, Nithin, et al.
Published: (2026)

Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics
by: Feng, Haodong, et al.
Published: (2025)

Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control
by: Vaillant, Mikael, et al.
Published: (2025)

Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
by: Vishwasrao, Abhijeet, et al.
Published: (2025)

Revealing Low-Dimensional Structure in 2D Richtmyer-Meshkov Instabilities via Parametric Reduced-Order Modeling
by: Messenger, Daniel, et al.
Published: (2025)

PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
by: Radler, Andreas, et al.
Published: (2025)

Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
by: Tonti, Federica, et al.
Published: (2025)

LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
by: Li, Yue
Published: (2025)

Pi-fusion: Physics-informed diffusion model for learning fluid dynamics
by: Qiu, Jing, et al.
Published: (2024)

Thin ring wing as a means of flow improvement upstream of a propeller
by: Sluchak, Vladimir
Published: (2025)