:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Sardar, Mohammed, Zimoń, Małgorzata J., Draycott, Samuel, Revell, Alistair, Skillen, Alex
Format:	Preprint
Publié:	2025
Sujets:	Fluid Dynamics Machine Learning
Accès en ligne:	https://arxiv.org/abs/2512.24813
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
par: Sardar, Mohammed, et autres
Publié: (2023)

Concerning the Use of Turbulent Flow Data for Machine Learning
par: Sardar, Mohammed, et autres
Publié: (2024)

Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation
par: Yeo, Kyongmin, et autres
Publié: (2024)

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
par: Kohl, Georg, et autres
Publié: (2023)

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

Turbulence Scaling from Deep Learning Diffusion Generative Models
par: Whittaker, Tim, et autres
Publié: (2023)

Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
par: Chu, Minghan, et autres
Publié: (2025)

CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
par: Liu, Xin-Yang, et autres
Publié: (2024)

Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks
par: Kurz, Marius, et autres
Publié: (2025)

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

Learned Coarse Models for Efficient Turbulence Simulation
par: Stachenfeld, Kimberly, et autres
Publié: (2021)

Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models
par: Li, Tianyi, et autres
Publié: (2024)

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

Unfolding Time: Generative Modeling for Turbulent Flows in 4D
par: Saydemir, Abdullah, et autres
Publié: (2024)

Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent Regime
par: Markmann, Thorben, et autres
Publié: (2025)

A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
par: Menicali, Luca, et autres
Publié: (2025)

From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
par: Lienen, Marten, et autres
Publié: (2023)

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
par: Liu, Xuemin, et autres
Publié: (2025)

Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
par: Dehtyriov, Daniel, et autres
Publié: (2026)

Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance
par: Wu, Hao, et autres
Publié: (2024)

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

Synthetic Lagrangian Turbulence by Generative Diffusion Models
par: Li, Tianyi, et autres
Publié: (2023)

Pixel-Resolved Long-Context Learning for Turbulence at Exascale: Resolving Small-scale Eddies Toward the Viscous Limit
par: Yin, Junqi, et autres
Publié: (2025)

Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution
par: Balla, Julia, et autres
Publié: (2025)

Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence
par: Koh, Christopher, et autres
Publié: (2024)

Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
par: Straat, Michiel, et autres
Publié: (2025)

Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
par: Grogan, Cody, et autres
Publié: (2025)

Differentiable Turbulence: Closure as a partial differential equation constrained optimization
par: Shankar, Varun, et autres
Publié: (2023)

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

Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations
par: Zhuang, Yilin, et autres
Publié: (2024)

Unstable magnetic reconnection self-generates turbulence
par: Williams, Nick, et autres
Publié: (2026)

SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
par: Abid, Muhammad, et autres
Publié: (2026)

FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation
par: Dai, Yilong, et autres
Publié: (2026)

Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
par: Shende, Takshak, et autres
Publié: (2026)

BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements
par: Veremchuk, Maksym, et autres
Publié: (2026)

Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements
par: Dang, Hiep Vo, et autres
Publié: (2024)

Learning multi-phase flow and transport in fractured porous media with auto-regressive and recurrent graph neural networks
par: Kobaisi, Mohammed Al, et autres
Publié: (2025)

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

On the joint estimation of flow fields and particle properties from Lagrangian data
par: Zhou, Ke, et autres
Publié: (2025)

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