Image de couverture de livre

Global Description of Flutter Dynamics via Koopman Theory

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Song, Jiwoo, Huang, Daning
Format:	Preprint
Publié:	2025
Sujets:	Fluid Dynamics Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting
par: Grozavescu, Rares, et autres
Publié: (2026)

Modal Analysis of Spatiotemporal Data via Multivariate Gaussian Process Regression
par: Song, Jiwoo, et autres
Publié: (2024)

Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection
par: Markmann, Thorben, et autres
Publié: (2024)

Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
par: Gupta, Priyam, et autres
Publié: (2023)

Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
par: Ma, Sicheng, et autres
Publié: (2026)

Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
par: Qian, Yixiao, et autres
Publié: (2026)

Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
par: Dong, Guodan, et autres
Publié: (2026)

Flutter stability of twin-box bridge decks
par: Rønne, Maja, et autres
Publié: (2024)

Flutter Limitation of Drag Reduction by Elastic Reconfiguration
par: Boukor, Maryam, et autres
Publié: (2023)

LGFNet: Local-Global Fusion Network with Fidelity Gap Delta Learning for Multi-Source Aerodynamics
par: Zhu, Qinye, et autres
Publié: (2026)

P-Bifurcations in Stochastic Flutter Model Under Turbulence
par: Tanweer, Sunia, et autres
Publié: (2025)

Predicting Flow Dynamics using Diffusion Models
par: Gachnang, Yannick, et autres
Publié: (2025)

Inpainting Computational Fluid Dynamics with Deep Learning
par: Shu, Dule, et autres
Publié: (2024)

Transported Memory Networks accelerating Computational Fluid Dynamics
par: Schulz, Matthias, et autres
Publié: (2025)

OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
par: Zhang, Rui, et autres
Publié: (2025)

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems
par: Yang, Tianyue, et autres
Publié: (2026)

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
par: Toshev, Artur P., et autres
Publié: (2024)

DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
par: Ma, Qilong, et autres
Publié: (2024)

DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
par: Luo, Dongyu, et autres
Publié: (2024)

Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
par: Lee, Minseo, et autres
Publié: (2026)

Stabilizing the Maximal Entropy Moment Method for Rarefied Gas Dynamics at Single-Precision
par: Zheng, Candi, et autres
Publié: (2023)

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
par: de Aguiar, Diego A., et autres
Publié: (2024)

Towards Scalable One-Step Generative Modeling for Autoregressive Dynamical System Forecasting
par: Yang, Tianyue, et autres
Publié: (2026)

Analysis of tidal flows through the Strait of Gibraltar using Dynamic Mode Decomposition
par: Dias, Sathsara, et autres
Publié: (2023)

Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
par: Schwarz, Henning, et autres
Publié: (2026)

LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics
par: Xiao, Qisong, et autres
Publié: (2026)

Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
par: Maric, Tomislav, et autres
Publié: (2024)

A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
par: Gao, Rui, et autres
Publié: (2022)

Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
par: Chakraborty, Dibyajyoti, et autres
Publié: (2026)

Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
par: Chou, Yi En, et autres
Publié: (2025)

Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
par: Huang, Yunfei, et autres
Publié: (2025)

Model-Agnostic AI Framework with Explicit Time Integration for Long-Term Fluid Dynamics Prediction
par: Yang, Sunwoong, et autres
Publié: (2024)

Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics
par: Jia, Wang, et autres
Publié: (2024)

Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
par: Xu, Weiming, et autres
Publié: (2023)

Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing
par: Wang, Qiulei, et autres
Publié: (2023)

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
par: Ashton, Neil, et autres
Publié: (2026)

Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulations
par: Caron, Clément, et autres
Publié: (2024)

Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds
par: Trupin, Noah, et autres
Publié: (2026)

Equation identification for fluid flows via physics-informed neural networks
par: New, Alexander, et autres
Publié: (2024)

Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
par: Sardar, Mohammed, et autres
Publié: (2025)