Computational Fluid Dynamics

Also known as CFD

Computational fluid dynamics (CFD) uses numerical methods to solve equations describing fluid motion, turbulence, heat transfer, and related processes. Finite-volume and finite-element solvers, alongside RANS, LES, and DNS, provide established ways to simulate flows; neural operators, graph networks, physics-informed neural networks, and generative models are increasingly used as surrogates or for reconstruction. These approaches can reduce the cost of simulation, support design and flow control, and infer fields from sparse measurements. A central challenge is balancing speed with physical consistency and reliable generalization across geometries and regimes, especially over long predictions; learned models still need validation against trusted simulations and experiments.

Latest papers

279 papers37 papers in the last four weeks, up 9% on the four weeks before. 0.2% of all new papers.

Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks
Oct 26, 2026Luca Biferale, Michele BuzzicottiGenerative Modeling Generative AI Evaluation
Department of Physics and INFN, University of Rome Tor Vergata, Rome, Italy
Neural Eigenfluid Bases: Spectral Objectives, Gauge, and Topology
Oct 24, 2026Baiming Zhang, Shiying Xiong, Zhecheng WangReduced-Order Modeling Computational Fluid Dynamics
Zhejiang University · University of Toronto
FG-ConvNO: A Geometry-Aware Neural Operator for Propeller CFD Prediction
Oct 24, 2026Yichen Di, Valentin Duruisseaux, Di Zhou +4Neural Operators Computational Fluid Dynamics
Tsinghua University · California Institute of Technology · University of Tennessee +1
Flow Mesh: A Velocity-Regularized Flow Matching Framework for Stable Distributional Modeling of Physical Fields on Unstructured Meshes
Oct 23, 2026Siddharth Verma, Jaewan Park, Jay Phil Yoo +3Generative Modeling Flow-Based Generative Modeling
Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai, India · National Center for Supercomputing Applications, Urbana, IL, USA · Grainger College of Engineering, Mechanical Science & Engineering Department, University of Illinois Urbana-Champaign, Urbana, IL, USA +3
Let's Do Turbulence Right: In Space and Time!
Oct 23, 2026Maximilian Plattner, Fabian Paischer, Johannes BrandstetterComputational Fluid Dynamics
ELLIS Unit, LIT AI Lab, Institute for Machine Learning, JKU Linz · Mistral AI
CoPiT: A conditional Position-induced Transformer for aerodynamic field prediction on unstructured meshes
Oct 23, 2026Junfeng Chen, Yongchen Fan, Zhonghua Qiao +1Transformer Neural Operator Neural Operators
Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China · Department of Mathematics and Shenzhen International Center for Mathematics, Southern University of Science and Technology, Shenzhen 518055, China
Solving and Learning Navier-Stokes Dynamics with the Neural Basis Method
Oct 23, 2026Yuhe Wang, Zhu Wang, Min WangNeural PDE Solvers Computational Fluid Dynamics
Institute for Scientific Computation, Texas A&M University, College Station, TX 77843, USA · Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA · Department of Mathematics, University of Houston, TX 77204, USA
RheOPT: Few-Shot Non-Newtonian Flow Prediction via Newtonian Pretraining of RheOFormer
Oct 22, 2026Maedeh Saberi, Amir Barati Farimani, Safa JamaliNeural Operators Computational Fluid Dynamics
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA · Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Local and Sparse Kernel Operator Learning for Incompressible Flows: An Operator Lagrange Formulation
Oct 21, 2026Ramansh Sharma, Matthew Lowery, Houman Owhadi +1PDE Operator Learning Kernel Methods
Kahlert School of Computing, University of Utah, UT, USA · Department of Computing and Mathematical Sciences, California Institute of Technology, CA, USA
Neural Operator Splitting for Fluid Dynamics in Complex Geometry
Oct 20, 2026Jianxiang Li, Xi Deng, Hong Chul Nam +1PDE Operator Learning Neural Operators
New York University
Machine-learned spectral PDE solvers for fluids and plasmas
Oct 19, 2026Mellow Baixuan Chen, Alan A. KaptanogluPDE Solving PDE Discovery
Courant Institute of Mathematical Sciences, New York University, New York, 10012, NY, USA · William E. Boeing Department of Aeronautics and Astronautics, University of Washington, Seattle, 98195, Washington, USA
GeoDiv KAN Solver for Time Fractional Navier Stokes Equations on Complex Domains to Realistic CFD Reconstruction
Oct 19, 2026Yatin Kumar, Shivani Saini, Ramesh Kumar VatsNeural PDE Solvers Partial Differential Equations
Department of Mathematics and Scientific Computing, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, 177005, India
CIS-Transolver: A Cross-Layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction
Oct 19, 2026Jianglong Liu, Zhicheng Zhu, Jia Hao +1Neural Operators Transformer Neural Operator
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Data-Driven Prediction of Optimal MPI Rank Placement and Node Count for Memory-Bound CFD on AMD EPYC Clusters
Oct 16, 2026Marcin Lawenda, Łukasz Szustak, Aleksandra KrasickaHigh-Performance Computing Adaptive Rank Allocation
Poznan Supercomputing and Networking Center, Jana Pawła II 10, 61-139 Poznań, Poland · Czestochowa University of Technology, Dąbrowskiego 69, 42-201 Częstochowa, Poland
How the elastic axis folds the transonic flutter boundary: shock energy transfer and conformally calibrated active learning
Oct 16, 2026Ashray Saxena, Sharad ShrivastavaFluid-Structure Interaction Computational Fluid Dynamics
Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani, Vidya Vihar, Pilani, Rajasthan 333031, India
Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes
Oct 14, 2026Stavros Nousias, Akis Nousias, Konstantinos GkrispanisPDE Surrogate Modeling Mixture-of-Experts Models
Technical University of Munich · K3Y Labs
Extreme-Scale Super-Resolution of Homogeneous Turbulence from a Single Realization
Oct 14, 2026Arth Sojitra, Muhammad Abid, Omer SanComputational Fluid Dynamics Image Super-Resolution
Department of Mechanical and Aerospace Engineering University of Tennessee, Knoxville Knoxville, TN 37996, USA
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics
Oct 13, 2026Hanlin Kong, Jianan Wei, Yuntian Chen +2Computational Fluid Dynamics Physics-Informed ML
State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, Zhejiang, China · Shanghai Innovation Institute, Shanghai, China · Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, China +1
Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting
Oct 12, 2026Ramzi Idoughi, Radouan Boukharfane, Peter WonkaSet Transformer Computational Fluid Dynamics
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco
Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics
Oct 12, 2026Kiet Bennema ten Brinke, Rachna Ramesh, Douwe Orij +2Neural Surrogate Modeling Computational Fluid Dynamics
Department of Mathematics and Computer Science, Eindhoven University of Technology · Department of Chemical Engineering and Chemistry, Eindhoven University of Technology
CLNO: Conservative Local Neural Operator for Compressible Flow Prediction
Oct 12, 2026Buxuan Wang, Rongzu Li, Qingsong ZouPDE Operator Learning Neural Operators
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries
Oct 8, 2026Weizheng Zhang, Xunjie Xie, Hao Pan +1Physics-Informed ML Neural PDE Solvers
Shandong University · Tsinghua University
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
Oct 7, 2026Peng Liu, Shaoxiang Qin, Theodore Potsis +3Computational Fluid Dynamics Conditional Flow Matching
Concordia University, Centre for Zero Energy Building Studies, Department of Building, Civil and Environmental Engineering, Montreal H3G 1M8, Canada · McGill University, School of Computer Science, Montreal, H3A 0E9, Canada
Cross-Domain Pretraining for Steady-State Neural CFD Surrogates
Oct 7, 2026Anthony Zhou, Amir Barati Farimani, Shirley Ho +1Cross-Domain Generalization PDE Surrogate Modeling
Carnegie Mellon University · New York University · Polymathic AI +4
PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
Oct 7, 2026Chunyang Wang, Mingrui Zhang, Yuyan Zhang +5Porous Media Flow Neural Surrogate Modeling
Imperial College London · Stanford University · EarthFlow AI, Inc. +1
Exact SO(3)-Equivariant Isotropic Kernels for Rotation-Robust Neural Dynamics
Oct 7, 2026Ridham PatelPDE Surrogate Modeling PDE Operator Learning
Indian Institute of Technology Gandhinagar, Gandhinagar, India
Tri-Directional Sub-Voxel Displacement and Ipsilateral Territory Decoupling for 3D Bilateral Coronary Artery Tree Reconstruction in Cardiac CT Angiography
Oct 5, 2026Zeyu Jia, Xin MingCoronary Angiography Computational Fluid Dynamics
School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, China
Shaping the Wind: Nested Potentials for Kinematically Admissible Urban Wind Prediction
Oct 4, 2026Yidi Wang, Yunhe Zhang, Jiawei Gu +2Computational Fluid Dynamics PDE Surrogate Modeling
The State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau · School of Computing and Information Technology, Great Bay University
Advectra: Asymmetric Latent Transport for Non-Stationary Physics
Oct 4, 2026Nodens Koren, Thomas Hofmann, Georgios KissasPDE Operator Learning Neural Operators
Department of Computer Science ETH Zürich Zürich, Switzerland · Swiss Data Science Center ETH Zürich Zürich, Switzerland
Do Neural PDE Solvers Learn the Right Dynamics?
Oct 3, 2026Haonan Li, Yue Song, Bin Yang +1Neural PDE Solvers Dynamical Systems
Center for Combustion Energy, Tsinghua University · College of AI, Tsinghua University · Department of Mechanical Engineering, University College London

Oct 26, 2026J/K move · Enter open · S save

Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks

Luca Biferale, Michele Buzzicotti

Department of Physics and INFN, University of Rome Tor Vergata, Rome, Italy

Generative Modeling Generative AI Evaluation Computational Fluid Dynamics Diffusion Models Score-Based Generative Modeling

Open paper AskPDF arXiv

Generative AI offers new approaches to turbulence problems in which incomplete observations or unresolved d.o.f. admit multiple physically plausible solutions. Potential applications include stochastic closures, super-resolution, data assimilation, uncertainty quantification, and the generation of Eulerian and Lagrangian flow ensembles. However, turbulence is not a conventional image-generation problem. Its multiscale dynamics, intermittency, extreme events, rough velocity fields and intrinsically stochastic transport make visual realism, pointwise error, and low-order spectral agreement insufficient measures of physical fidelity. We examine where generative AI provide novel capabilities with emphasis on diffusion models, score-based methods, and related probability-transport formulations. We discuss their use in unconditional generation, probabilistic reconstruction, reduced-order modeling, temporal evolution, and mesh-free representations. We argue that validation must address turbulence properties: energy transfer, structure functions, anomalous scaling, pdf tails, coherent structures, conservation laws, temporal consistency, ensemble diversity and generalization. Finally, we discuss potential limitations in validation, baseline, data availability and reproducibility, and the need for a benchmark ecosystem combining open data, standardized challenges, hidden tests, physics-aware metrics, and documented failures. This paper serves as a bridge between two communities. For AI practitioners, we provide a fair and accessible introduction to turbulence, essential for appreciating the challenges posed by the field and for assessing the physical relevance of generative models. For turbulent practitioners, we provide an introduction to the key technological aspects of Generative AI, clarifying what these tools actually do, and where their potential and current limitations lie.