Computational Fluid Dynamics

Also known as CFD

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Overview

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

CardsList

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

  1. Topological Characterization of Churn Flow and Unsupervised Correction to the Wu Flow-Regime Map in Small-Diameter Vertical Pipes

    Apr 7, 2026Brady Koenig, Sushovan Majhi, Atish Mitra +2Computational Fluid DynamicsTopological Data Analysis

    Department of Petroleum Engineering, Montana Technological University, USA · Data Science, George Washington University, USA · Department of Mathematical Sciences, Montana Technological University, USA

  2. Asymptotic-Preserving Neural Networks for Viscoelastic Parameter Identification in Multiscale Blood Flow Modeling

    Apr 7, 2026Giulia Bertaglia, Raffaella Fiamma CabiniParameter EstimationComputational Fluid Dynamics

    Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121, Ferrara, Italy

  3. Optimal-Transport-Guided Functional Flow Matching for Turbulent Field Generation in Hilbert Space

    Apr 7, 2026Li Kunpeng, Wan Chenguang, Qu Zhisong +5Flow-Based Generative ModelingFlow Matching

  4. Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies

    Apr 7, 2026Prashant Kumar, Rajesh RanjanDomain Decomposition MethodsNeural PDE Solvers

    Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India 208016

  5. A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems

    Apr 2, 2026Kiran Yalamanchi, Shivam Barwey, Ibrahim Jarrah +1Computational Fluid DynamicsPDE Surrogate Modeling

    Transportation and Power Systems Division, Argonne National Laboratory, Lemont, IL, USA

  6. Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors

    Apr 2, 2026M. Lo Verso, C. Introini, E. Cervi +3MagnetohydrodynamicsComputational Fluid Dynamics

    Department of Energy, Politecnico di Milano, Milano, 20133, Italy · MATHEP Group, Dept. of Energy "Galileo Ferraris", Politecnico di Torino, Torino, Italy · Autodesk Research, 6 Agar Street, London UK +1

  7. Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning

    Mar 31, 2026Eloghosa Ikponmwoba, Opeoluwa OwoyeleReinforcement LearningConstrained RL

    Department of Mechanical and Industrial Engineering Louisiana State University Baton Rouge, LA 70803

  8. LGFNet: Local-Global Fusion Network with Fidelity Gap Delta Learning for Multi-Source Aerodynamics

    Mar 31, 2026Qinye Zhu, Yu Xiang, Jun Zhang +1Multi-Scale Feature FusionMultifidelity Modeling

    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

  9. Lie Generator Networks for Nonlinear Partial Differential Equations

    Mar 31, 2026Shafayeth Jamil, Rehan KapadiaKoopman Operator LearningPDE Operator Learning

    Ming Hsieh Department of Electrical and Computer Engineering University of Southern California

  10. Data-informed lifting line theory

    Mar 30, 2026Arjun Sharma, Jonas A. Actor, Peter A. BoslerComputational Fluid DynamicsNeural Surrogate Modeling

    Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, 87185, USA

  11. FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes

    Mar 30, 2026David Ramos, Lucas Lacasa, Fermín Gutiérrez +2PDE Surrogate ModelingComputational Fluid Dynamics

    ETSIAE-UPM-School of Aeronautics, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros 3, E-28040 Madrid, Spain · Institute for Cross-Disciplinary Physics and Complex Systems (IFISC, CSIC-UIB), 07122 Palma de Mallorca (Spain) · Center for Computational Simulation, Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain

  12. Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection

    Mar 30, 2026Tim Plotzki, Sebastian PeitzRL ControlPDE Surrogate Modeling

    TU Dortmund University & Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany

  13. SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution

    Mar 30, 2026Muhammad Abid, Omer SanNeural OperatorsComputational Fluid Dynamics

    Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville, TN 37996, USA.

  14. Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates

    Mar 28, 2026Yeping Hu, Ruben Glatt, Shusen LiuComputational Fluid DynamicsPDE Surrogate Modeling

    Computational Engineering Division Lawrence Livermore National Laboratory Livermore, CA 94550 · Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore, CA 94550

  15. Anchored-Branched Steady-state WInd Flow Transformer (AB-SWIFT): a metamodel for 3D atmospheric flow in urban environments

    Mar 26, 2026Armand de Villeroché, Rem-Sophia Mouradi, Vincent Le Guen +5Neural Surrogate ModelingPDE Surrogate Modeling

    CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris, Île-de-France, France · EDF R&D, Île-de-France, France · SINCLAIR AI Laboratory, Saclay, Île-de-France, France +1

  16. DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting

    Mar 26, 2026Huanshuo Dong, Hao Wu, Hong Wang +2PDE Operator LearningNeural Operators

    University of Science and Technology of China, Hefei, China · Tsinghua University, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China +1

  17. Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

    Mar 25, 2026Xigui Li, Hongwei Zhang, Ruoxi Jiang +6Neural OperatorsComputational Fluid Dynamics

    Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China · Shanghai Academy of Artificial Intelligence for Science, Shanghai, China

  18. Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations

    Mar 24, 2026Sebastien Andre-Sloan, Dibyakanti Kumar, Alejandro F Frangi +1Generalization BoundsPhysics-Informed ML

    Department of Computer Science, The University of Manchester

  19. Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows

    Mar 22, 2026Janne Perini, Rafael Bischof, Moab Arar +4Video Diffusion ModelsPDE Surrogate Modeling

    1Computational Design Lab, ETH Zurich, Switzerland · 2Tel Aviv University, Israel · 3Architecture and Building Systems, ETH Zurich, Switzerland +2

  20. Modeling subgrid scale production rates on complex meshes using graph neural networks

    Mar 20, 2026Priyabrat Dash, Mathis Bode, Konduri AdityaComputational Fluid DynamicsGraph Neural Networks

    Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, KA, India · Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, 52425, NRW, Germany

  21. An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media

    Mar 20, 2026V. S. Maduri, K. B. NakshatralaNeural PDE SolversPorous Media Flow

    Department of Civil & Environmental Engineering University of Houston, Houston, Texas 77204, USA.

  22. Model Order Reduction of Cerebrovascular Hemodynamics Using POD_Galerkin and Reservoir Computing_based Approach

    Mar 19, 2026Rahul Halder, Arash Hajisharifi, Kabir Bakhshaei +1Reduced-Order ModelingPDE Surrogate Modeling

    Mathematics Area, mathLab, SISSA, via Bonomea 265, I-34136 Trieste, Italy · Biorobotics Institute, SMART Lab, Sant’Anna School of Advanced Studies, Pisa, Italy · Department of Computer Science, University of Pisa, Pisa, Italy

  23. Physics-integrated neural differentiable modeling for immersed boundary systems

    Mar 17, 2026Chenglin Li, Hang Xu, Jianting Chen +1PDE Surrogate ModelingComputational Fluid Dynamics

    State Key Lab of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · State Key Laboratory of Maritime Technology and Safety, Shanghai Ship and Shipping Research Institute Co., Ltd, Shanghai 200135, China.

  24. A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames

    Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4AutoencodersPDE Surrogate Modeling

    Department of Aerospace Engineering, KAIST, Daejeon, Republic of Korea · Clean Energy Research Platform (CERP), KAUST, Thuwal, Saudi Arabia · Institute for Reactive Flows (IRST), University of Stuttgart, Pfaffenwaldring 31, 70569 Stuttgart, Germany

  25. Hierarchy of extreme-event predictability in turbulence revealed by machine learning

    Mar 14, 2026Yuxuan Yang, Chenyu Dong, Gianmarco MengaldoChaotic Time-Series PredictionComputational Fluid Dynamics

    National University of Singapore Singapore

  26. Learning Pore-scale Multiphase Flow from 4D Velocimetry

    Mar 12, 2026Chunyang Wang, Linqi Zhu, Yuxuan Gu +9Porous Media FlowComputational Fluid Dynamics

    Department of Earth Science and Engineering, Imperial College London. · Department of Geology, Ghent University. · Department of Energy Science and Engineering, Stanford University. +2

  27. A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media

    Mar 11, 2026V. S. Maduri, K. B. NakshatralaPorous Media FlowComputational Fluid Dynamics

    Graduate Student, Department of Civil & Environmental Engineering University of Houston, Houston, Texas 77204. · Department of Civil & Environmental Engineering University of Houston, Houston, Texas 77204.

  28. Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics

    Mar 11, 2026M. Lo Verso, C. Introini, E. Cervi +3MagnetohydrodynamicsNeural Surrogate Modeling

    Department of Energy, Politecnico di Milano, Milano, 20133, Italy · MATHEP Group, Dept. of Energy "Galileo Ferraris", Politecnico di Torino, Torino, Italy · Autodesk Research, 6 Agar Street, London UK +1

  29. UniPINN: A Unified PINN Framework for Multi-task Learning of Diverse Navier-Stokes Equations

    Mar 11, 2026Dengdi Sun, Jie Chen, Xiao Wang +1Physics-Informed MLMulti-Task Learning

    Key Laboratory of Intelligent Computing & Signal Processing (ICSP), Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China · Anhui Provincial Key Laboratory of Multimodal Cognitive Computing, School of Computer Science and Technology, Anhui University, Hefei 230601, China

  30. Flow Field Reconstruction via Voronoi-Enhanced Physics-Informed Neural Networks with End-to-End Sensor Placement Optimization

    Mar 10, 2026Renjie Xiao, Bingteng Sun, Yiling Chen +3Computational Fluid DynamicsPhysics-Informed ML

    Advanced Gas Turbine Laboratory, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, 100190, China · National Key Laboratory of Science and Technology on Advanced Light-duty Gas-turbine, Beijing, 100190, China · School of Computer Science and Technology Shandong University Qingdao Shandong 266237 China +3