Imagen de Portada

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kohl, Georg, Chen, Li-Wei, Thuerey, Nils
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Fluid Dynamics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models
por: Liu, Qiang, et al.
Publicado: (2023)

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

FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation
por: Dai, Yilong, et al.
Publicado: (2026)

Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
por: Yang, Yunjia, et al.
Publicado: (2026)

From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
por: Lienen, Marten, et al.
Publicado: (2023)

Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision
por: Franz, Aleksandra, et al.
Publicado: (2023)

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

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

LAViG-FLOW: Latent Autoregressive Video Generation for Fluid Flow Simulations
por: De Pellegrini, Vittoria, et al.
Publicado: (2026)

Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
por: Dehtyriov, Daniel, et al.
Publicado: (2026)

Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models
por: Li, Tianyi, et al.
Publicado: (2024)

Unfolding Time: Generative Modeling for Turbulent Flows in 4D
por: Saydemir, Abdullah, et al.
Publicado: (2024)

Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models
por: Sardar, Mohammed, et al.
Publicado: (2025)

CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
por: Liu, Xin-Yang, et al.
Publicado: (2024)

Learned Coarse Models for Efficient Turbulence Simulation
por: Stachenfeld, Kimberly, et al.
Publicado: (2021)

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
por: Liu, Xuemin, et al.
Publicado: (2025)

Predicting Flow Dynamics using Diffusion Models
por: Gachnang, Yannick, et al.
Publicado: (2025)

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

Physics-enhanced Neural Operator for Simulating Turbulent Transport
por: Chen, Shengyu, et al.
Publicado: (2024)

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

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

FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
por: Wang, Haixin, et al.
Publicado: (2025)

Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks
por: Kurz, Marius, et al.
Publicado: (2025)

DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
por: Luo, Dongyu, et al.
Publicado: (2024)

Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
por: Chu, Minghan, et al.
Publicado: (2025)

Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection
por: Markmann, Thorben, et al.
Publicado: (2024)

Geometry-aware PINNs for Turbulent Flow Prediction
por: Ghosh, Shinjan, et al.
Publicado: (2024)

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

Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
por: Mukhopadhyay, Payel, et al.
Publicado: (2026)

Turbulence Scaling from Deep Learning Diffusion Generative Models
por: Whittaker, Tim, et al.
Publicado: (2023)

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

Towards Scalable One-Step Generative Modeling for Autoregressive Dynamical System Forecasting
por: Yang, Tianyue, et al.
Publicado: (2026)

EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale
por: Du, Yiheng, et al.
Publicado: (2025)

Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds
por: Trupin, Noah, et al.
Publicado: (2026)

Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction
por: Francés-Belda, Víctor, et al.
Publicado: (2026)

The compressible Neural Particle Method for Simulating Compressible Viscous Fluid Flows
por: Shibukawa, Masato, et al.
Publicado: (2025)

FoilDiff: A Hybrid Transformer Backbone for Diffusion-based Modelling of 2D Airfoil Flow Fields
por: Ogbuagu, Kenechukwu, et al.
Publicado: (2025)

Graph Deep Learning for Intracranial Aneurysm Blood Flow Simulation and Risk Assessment
por: Garnier, Paul, et al.
Publicado: (2025)

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
por: Rabeh, Ali, et al.
Publicado: (2024)

Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution
por: Balla, Julia, et al.
Publicado: (2025)