Computational Fluid Dynamics

Also known as CFD

Ask about this topic

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

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

  1. 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

  2. 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

  3. MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries

    Mar 9, 2026Weizheng Zhang, Xunjie Xie, Hao Pan +4Neural PDE SolversPDE Solving

    Shandong University, Qingdao, China · Tsinghua University, Beijing, China · Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, China

  4. Generative prediction of laser-induced rocket ignition with dynamic latent space representations

    Mar 8, 2026Tony Zahtila, Ettore Saetta, Murray Cutforth +3Neural Surrogate ModelingPDE Surrogate Modeling

    Center for Turbulence Research, Stanford University, USA · University of Naples Federico II, Italy

  5. Prediction of Steady-State Flow through Porous Media Using Machine Learning Models

    Mar 6, 2026Jinhong Wang, Matei C. Ignuta-Ciuncanu, Ricardo F. Martinez-Botas +1Porous Media FlowPDE Surrogate Modeling

    Department of Mechanical Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, , United Kingdom

  6. Jacobian-Adaptive Weighting for Stability: Enhancing Long-term Rollout of Neural Partial Differential Equation Solvers via Spatially-Adaptive Regularization

    Mar 4, 2026Fengxiang Nie, Yasuhiro SuzukiNeural PDE SolversAlgorithmic Stability

    University of Hiroshima

  7. Latent attention on masked patches for flow reconstruction

    Mar 2, 2026Ben Eze, Luca Magri, Andrea NóvoaComputational Fluid DynamicsVision Transformer

    Aeronautics Dept., Imperial College London, Exhibition Rd, London SW7 2BX, UK · DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi, 24, Torino, 10129, Italy · I-X, Imperial College London, 84 Wood Lane, London W12 0BZ, UK

  8. Structure-preserving Randomized Neural Networks for Incompressible Magnetohydrodynamics Equations

    Mar 1, 2026Yunlong Li, Fei Wang, Lingxiao LiStructure-Preserving Neural NetworksMagnetohydrodynamics

    School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China. · School of Mathematics and Statistics & State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China. · School of Mathematics and Statistics, Henan University, Kaifeng, Henan 475004, China. +1

  9. Super-resolution of turbulent reacting flows on complex meshes using graph neural networks

    Mar 1, 2026Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis +1Computational Fluid DynamicsGraph Neural Networks

    FLAME Laboratory, Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India · CAPS Laboratory, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland · Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany

  10. Data-Free Finite-Volume Convolutional Neural Networks for Steady Compressible Euler Flow around a Circular Cylinder

    Mar 1, 2026Ryosuke YanoNeural PDE SolversPhysics-Informed ML

    Tokio Marine dR Co., Ltd., 1-5-1, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.

  11. Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction

    Feb 28, 2026Shilong Tao, Zhe Feng, Shaohan Chen +3Fluid-Structure InteractionPhysics-Based Simulation

    School of Computer Science, Peking University · Global Innovation Exchange, Tsinghua University · School of Electrical and Computer Science, University of Southampton

  12. BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements

    Feb 27, 2026Maksym Veremchuk, K. Andrea Scott, Zhao PanOperator LearningComputational Fluid Dynamics

    Department of Mechanical and Mechatronics Engineering, University of Waterloo

  13. Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations

    Feb 26, 2026Abhijeet Vishwasrao, Francisco Giral, Mahmoud Golestanian +8AI Agents for Scientific DiscoveryPhysics-Based Simulation

    University of Michigan · Universidad Polit´ecnica de Madrid · Purdue University +3

  14. Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks

    Feb 26, 2026Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman +4Data AssimilationEnsemble Kalman Filtering

    Scripps Institution of Oceanography, University of California San Diego, La Jolla, 92093, CA, USA · Computing + Mathematical Sciences, California Institute of Technology, Pasadena, 91125, CA, USA · Department of Mechanical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA

  15. Learning Physical Operators using Neural Operators

    Feb 26, 2026Vignesh Gopakumar, Ander Gray, Dan Giles +5PDE Surrogate ModelingPhysics-Informed Neural Operators

    UCL Centre for Artificial Intelligence · UK Atomic Energy Authority · LIX, CNRS, ´Ecole Polytechnique +2

  16. Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

    Feb 25, 2026Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4Surrogate ModelingPDE Surrogate Modeling

    Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ UK · Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, EH14 1AS UK · Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ UK +2

  17. From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators

    Feb 25, 2026Zhihao Li, Yu Feng, Zhilu Lai +1PDE Operator LearningNeural Operators

    The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China

  18. Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction

    Feb 25, 2026Zhihao Li, Shengwei Dong, Chuang Yi +5Physics-Informed Diffusion ModelsImage Super-Resolution

    The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · SandGold AI Research, Guangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China +1

  19. WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs

    Feb 24, 2026Lisa Lüddecke, Michael Hohmann, Sebastian Eilermann +4Surrogate ModelingComputational Fluid Dynamics

    Hydraulic Engineering Section, Civil Engineering, Helmut-Schmidt-University, Hamburg, Germany · Institute for Artificial Intelligence, Helmut-Schmidt-University, Hamburg, Germany · Professorship of Computer Science in Mechanical Engineering, Helmut-Schmidt-University, Hamburg, Germany +2

  20. A Deep Surrogate Model for Robust and Generalizable Long-Term Blast Wave Prediction

    Feb 20, 2026Danning Jing, Xinhai Chen, Xifeng Pu +5Neural Surrogate ModelingSpatiotemporal Forecasting

    National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha 410073, China · Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Changsha 410073, China · College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China +2

  21. Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method

    Feb 19, 2026Yuhe Wang, Min WangNeural PDE SolversPhysics-Informed ML

    Institute for Scientific Computation, Texas A&M University, College Station, TX 77843, USA · Department of Mathematics, University of Houston, Houston, TX 77204, USA

  22. Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds

    Feb 19, 2026Noah Trupin, Rahul Ghosh, Aadi JangidConstrained Generative ModelingPhysics-Informed Diffusion Models

    Department of Computer Science, Purdue University

  23. Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling

    Feb 19, 2026Xinghao Dong, Huchen Yang, Jin-long WuDiffusion Model SamplingFlow Matching

    Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, WI 53706

  24. Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine Learning, and Physics-Informed Methods

    Feb 17, 2026Tanxin Zhu, Emran Hossen, Chen Zhao +4Medical Image AnalysisPhysics-Informed ML

    Department of Applied Computing, Michigan Technological University, Houghton, MI, USA · Department of Computer Science, Kennesaw State University, Marietta GA, USA · Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA +3

  25. Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows

    Feb 17, 2026Xiao Xue, Tianyue Yang, Mingyang Gao +7Autoregressive DiffusionComputational Fluid Dynamics

    Centre for Computational Science, University College London, London, UK · Department of Earth Science and Engineering, Imperial College London, London, UK · Department of Chemical Engineering, University College London, London, UK +5

  26. Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows

    Feb 17, 2026Ramansh Sharma, Matthew Lowery, Houman Owhadi +1PDE Operator LearningOperator Learning

    Kahlert School of Computing University of Utah UT, USA · Department of Computing and Mathematical Sciences California Institute of Technology CA, USA

  27. Adjoint-based shape optimization of a ship hull using a Conditional Variational Autoencoder (CVAE) assisted propulsion surrogate model

    Feb 16, 2026Moloud Arian Maram, Georgios Bletsos, Thanh Tung Nguyen +3PDE Surrogate ModelingComputational Fluid Dynamics

    J.M. Voith SE & Co. KG, Alexanderstrasse 2, Heidenheim, 89522, Baden-Württemberg, Germany · Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology, Am Schwarzenberg-Campus 4, Hamburg, 21073, Germany

  28. Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements

    Jan 28, 2026Ofek Aloni, Barak FishbainDifferentiable PhysicsPhysics-Informed ML

    Department of Mathematics, Technion, Israel · Department of Civil and Environmental Engineering, Technion, Israel

  29. GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance

    Jan 16, 2026Francisco Giral, Álvaro Manzano, Ignacio Gómez +2Diffusion Model GuidanceData Assimilation

    Applied Mathematics Department, ETSIAE-School of Aeronautics, Universidad Politécnica de Madrid, Spain · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

  30. GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer

    Dec 23, 2025Corey Adams, Rishikesh Ranade, Ram Cherukuri +1Neural Surrogate ModelingStructural Mechanics

    NVIDIA Corporation, Santa Clara, US