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.

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 Solvers PDE Solving
Shandong University, Qingdao, China · Tsinghua University, Beijing, China · Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, China
Generative prediction of laser-induced rocket ignition with dynamic latent space representations
Mar 8, 2026Tony Zahtila, Ettore Saetta, Murray Cutforth +3Neural Surrogate Modeling PDE Surrogate Modeling
Center for Turbulence Research, Stanford University, USA · University of Naples Federico II, Italy
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 Flow PDE Surrogate Modeling
Department of Mechanical Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, , United Kingdom
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 Solvers Algorithmic Stability
University of Hiroshima
Latent attention on masked patches for flow reconstruction
Mar 2, 2026Ben Eze, Luca Magri, Andrea NóvoaComputational Fluid Dynamics Vision 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
Structure-preserving Randomized Neural Networks for Incompressible Magnetohydrodynamics Equations
Mar 1, 2026Yunlong Li, Fei Wang, Lingxiao LiStructure-Preserving Neural Networks Magnetohydrodynamics
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
Super-resolution of turbulent reacting flows on complex meshes using graph neural networks
Mar 1, 2026Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis +1Computational Fluid Dynamics Graph 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
Data-Free Finite-Volume Convolutional Neural Networks for Steady Compressible Euler Flow around a Circular Cylinder
Mar 1, 2026Ryosuke YanoNeural PDE Solvers Physics-Informed ML
Tokio Marine dR Co., Ltd., 1-5-1, Otemachi, Chiyoda-ku, Tokyo, 100-0004, Japan.
Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction
Feb 28, 2026Shilong Tao, Zhe Feng, Shaohan Chen +3Fluid-Structure Interaction Physics-Based Simulation
School of Computer Science, Peking University · Global Innovation Exchange, Tsinghua University · School of Electrical and Computer Science, University of Southampton
BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements
Feb 27, 2026Maksym Veremchuk, K. Andrea Scott, Zhao PanOperator Learning Computational Fluid Dynamics
Department of Mechanical and Mechatronics Engineering, University of Waterloo
Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations
Feb 26, 2026Abhijeet Vishwasrao, Francisco Giral, Mahmoud Golestanian +8AI Agents for Scientific Discovery Physics-Based Simulation
University of Michigan · Universidad Polit´ecnica de Madrid · Purdue University +3
Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks
Feb 26, 2026Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman +4Data Assimilation Ensemble 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
Learning Physical Operators using Neural Operators
Feb 26, 2026Vignesh Gopakumar, Ander Gray, Dan Giles +5PDE Surrogate Modeling Physics-Informed Neural Operators
UCL Centre for Artificial Intelligence · UK Atomic Energy Authority · LIX, CNRS, ´Ecole Polytechnique +2
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Feb 25, 2026Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4Surrogate Modeling PDE 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
From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
Feb 25, 2026Zhihao Li, Yu Feng, Zhilu Lai +1PDE Operator Learning Neural Operators
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China
Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction
Feb 25, 2026Zhihao Li, Shengwei Dong, Chuang Yi +5Physics-Informed Diffusion Models Image 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
WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs
Feb 24, 2026Lisa Lüddecke, Michael Hohmann, Sebastian Eilermann +4Surrogate Modeling Computational 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
A Deep Surrogate Model for Robust and Generalizable Long-Term Blast Wave Prediction
Feb 20, 2026Danning Jing, Xinhai Chen, Xifeng Pu +5Neural Surrogate Modeling Spatiotemporal 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
Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method
Feb 19, 2026Yuhe Wang, Min WangNeural PDE Solvers Physics-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
Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds
Feb 19, 2026Noah Trupin, Rahul Ghosh, Aadi JangidConstrained Generative Modeling Physics-Informed Diffusion Models
Department of Computer Science, Purdue University
Synergizing Transport-Based Generative Models and Latent Geometry for Stochastic Closure Modeling
Feb 19, 2026Xinghao Dong, Huchen Yang, Jin-long WuDiffusion Model Sampling Flow Matching
Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, WI 53706
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 Analysis Physics-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
Uni-Flow: a unified autoregressive-diffusion model for complex multiscale flows
Feb 17, 2026Xiao Xue, Tianyue Yang, Mingyang Gao +7Autoregressive Diffusion Computational 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
Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
Feb 17, 2026Ramansh Sharma, Matthew Lowery, Houman Owhadi +1PDE Operator Learning Operator Learning
Kahlert School of Computing University of Utah UT, USA · Department of Computing and Mathematical Sciences California Institute of Technology CA, USA
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 Modeling Computational 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
Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements
Jan 28, 2026Ofek Aloni, Barak FishbainDifferentiable Physics Physics-Informed ML
Department of Mathematics, Technion, Israel · Department of Civil and Environmental Engineering, Technion, Israel
GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance
Jan 16, 2026Francisco Giral, Álvaro Manzano, Ignacio Gómez +2Diffusion Model Guidance Data 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.
GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
Dec 23, 2025Corey Adams, Rishikesh Ranade, Ram Cherukuri +1Neural Surrogate Modeling Structural Mechanics
NVIDIA Corporation, Santa Clara, US
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Dec 19, 2025Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18RL Control RL Benchmarks
Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States · AI Institute in Dynamic Systems, University of Washington, Seattle, WA 98195, United States · Chair of Fluid Mechanics and Institute of Aerodynamics, RWTH Aachen University, Aachen, Germany +9
CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics
Nov 25, 2025Mohamed Elrefaie, Dule Shu, Matt Klenk +1Automotive Aerodynamics Neural Surrogate Modeling
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA · Schwarzman College of Computing, Massachusetts Institute of Technology, Cambridge, MA, USA · Future Product Innovation, Toyota Research Institute, Los Altos, CA, USA

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

Weizheng Zhang, Xunjie Xie, Hao Pan, Xiaowei Duan+3

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

Neural PDE Solvers PDE Solving Physics-Informed ML Computational Fluid Dynamics

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While Physics-Informed Neural Networks (PINNs) offer a mesh-free approach to solving fluid-flow PDEs, standard point-wise residual minimization suffers from convergence pathologies in topologically complex domains like Triply Periodic Minimal Surfaces (TPMS). The locality bias of point-wise constraints fails to propagate global information through tortuous channels, causing unstable gradients and conservation violations. To address this, we propose the Multi-scale Weak-form PINN (MUSA-PINN), which reformulates Navier-Stokes equation constraints as integral conservation laws over hierarchical spherical control volumes. We enforce continuity and momentum conservation via flux-balance residuals on control surfaces. Our method utilizes a three-scale subdomain strategy-comprising large volumes for long-range coupling, skeleton-aware meso-scale volumes aligned with transport pathways, and small volumes for local refinement-alongside a two-stage training schedule prioritizing continuity. Experiments on steady incompressible flow in TPMS geometries show MUSA-PINN outperforms state-of-the-art baselines, reducing relative errors by up to 93% and preserving mass conservation.