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.

Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining
Apr 30, 2026Manho Park, Christopher V. Rackauckas, Christopher W. TessumPDE Surrogate Modeling Computational Fluid Dynamics
The Grainger College of Engineering, Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics
Apr 30, 2026Bojun Zhang, Huiyu Yang, Yunpeng Wang +4Rotary Positional Embeddings Automotive Aerodynamics
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, 518055, China · Shenzhen Key Laboratory of Complex Aerospace Flows, Southern University of Science and Technology,Shenzhen 518055, China · Ningbo Key Laboratory of Advanced Manufacturing Simulation, Eastern Institute of Technology, Ningbo, 315200, China +1
Learning Neural Operator Surrogates for the Black Hole Accretion Code
Apr 28, 2026Matthias Nägele, Cedric Bös, Chester Tan +3Computational Fluid Dynamics Transformer Neural Operator
Institute for Theoretical Physics and Astrophysics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany · Chair for Machine Learning for Complex Networks, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
Apr 27, 2026Guodan Dong, Jianhua Qin, Chang XuFourier Neural Operator PDE Surrogate Modeling
College of Renewable Energy Hohai University Changzhou 213200 China · College of Water Conservancy and Hydropower Engineering Nanjing 210098 China · National Technology Innovation Center for Wind Power Hohai University Changzhou 213200 China
Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics
Apr 26, 2026Ashwin Suriyanarayanan, Dibyajyoti Chakraborty, Romit MaulikPhysics-Informed ML Computational Fluid Dynamics
School of Mechanical Engineering Purdue University West Lafayette, IN 47907 · College of Information Sciences and Technology Pennsylvania State University University Park, PA 16802
Physics-Informed Temporal U-Net for High-Fidelity Fluid Interpolation
Apr 25, 2026Eshwar R. A., Nevin Mathew Thomas, Nehal G +1U-Net Video Frame Interpolation
Department of Computer Science Engineering, PES University - Electronic City Campus, Bangalore 560100, India.
GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries
Apr 25, 2026Ruiling Jiang, Yong Zhang, Houbiao LiPDE Surrogate Modeling Generative Modeling
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China
Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen
Apr 24, 2026Leszek Siwik, Maciej Sikora, Natalia Leszczyńska +7Neural PDE Solvers Air Quality Prediction
AGH University of Krakow, Faculty of Computer Science, Al. Mickiewicza 30, Kraków, 30-059, Poland · Medical University of Silesia-Katowice, Faculty of Medical Sciences, ul. Poniatowskiego 15, Katowice, 40-055, Poland · The University Centre in Svalbard, Longyearbyen, Box 156 N-9171, Longyearbyen, Norway +4
Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces
Apr 22, 2026Ganesh Sahadeo Meshram, Partha Pratim Chakrabarti, Suman ChakrabortyPDE Operator Learning Physics-Informed Neural Operators
Department of Mechanical Engineering, IIT Kharagpur, Kharagpur, India · Department of Computer Science & Engineering, IIT Kharagpur, Kharagpur, India
CO $_2$ sequestration hybrid solver using isogeometric alternating-directions and collocation-based robust variational physics informed neural networks (IGA-ADS-CRVPINN)
Apr 22, 2026Askold Vilkha, Tomasz Służalec, Marcin Łoś +1Neural PDE Solvers Porous Media Flow
AGH University of Krakow, Faculty of Computer Science, Al. Mickiewicza 2026 30, Kraków, 30-059, Poland
AI models of unstable flow exhibit hallucination
Apr 22, 2026Ramdhan Wibawa, Birendra JhaScientific ML Neural Operators
Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, 90089, CA, USA.
FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction
Apr 21, 2026Xiaowen Zhang, Ziming Zhou, Fengnian Zhao +1Efficient Inference PDE Surrogate Modeling
Shanghai Jiao Tong University · University of Michigan
Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
Apr 20, 2026Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti +5PDE Surrogate Modeling Graph Neural Operators
IBM Research · Dallara Automobili
Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design
Apr 20, 2026Yunjia Yang, Babak Gholami, Caglar Gurbuz +2Surrogate Modeling Transformer
Technical University of Munich, Garching, D-85748, Germany · BMW Group, Bremer Str. 6, Munich, D-80788, Germany
TPS-CalcBench: A Benchmark and Diagnostic Evaluation Framework for LLM Analytical Calculation Competence in Hypersonic Thermal Protection System Engineering
Apr 20, 2026Jinglai Zheng, Chuhan Qiao, Haiming HuangSynthetic Benchmark Generation LLM Safety Evaluation
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Apr 18, 2026Khemraj Shukla, Zongren Zou, Theo Kaeufer +2Uncertainty Quantification Deep Ensembles
Division of Applied Mathematics, Brown University, Providence, RI, USA · Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
FlowRefiner: Flow Matching-Based Iterative Refinement for 3D Turbulent Flow Simulation
Apr 18, 2026Yilong Dai, Yiming Sun, Yiheng Chen +3Flow Matching Neural PDE Solvers
University of Alabama · University of Pittsburgh
Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
Apr 15, 2026Sudeepta Mondal, Soumalya SarkarPDE Surrogate Modeling Physics-Informed ML
Raytheon Technologies Research Center, 411 Silver Lane, East Hartford, CT 06108.
Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
Apr 15, 2026Anran Jiao, Wengyao Jiang, Xiaoyi Lu +2Radiative Transfer PDE Surrogate Modeling
Department of Statistics and Data Science, Yale University, New Haven, CT 06511, USA · Department of Biostatistics, Yale University, New Haven, CT 06511, USA · FM, Research Division, Norwood, MA 02062, USA +1
Applying Two-Grid Preconditioner for Subsurface Flow Simulation using Attention-enhanced Hybrid Network to Accelerate Multiscale Discretization in High-contrast Media
Apr 15, 2026Peiqi Li, Jie Chen, Shubin FuPorous Media Flow Finite Element Method
Department of Applied Mathemaitcs, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China · Department of Mathematical Sciences, University of Liverpool, L69 3BX, Liverpool, China · Eastern Institute for Advanced Study, Eastern Institute of Technology, 315200, Ningbo, China
Data-driven Learning of Probabilistic Model of Binary Droplet Collision for Spray Simulation
Apr 15, 2026Weiming Xu, Tao Yang, Peng ZhangComputational Fluid Dynamics
Department of Mechanical Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, 999077, Hong Kong · Shenzhen Research Institute, City University of Hong Kong, Shenzhen, 518057, P. R. China
AeTHERON: Autoregressive Topology-aware Heterogeneous Graph Operator Network for Fluid-Structure Interaction
Apr 15, 2026Sushrut KumarFluid-Structure Interaction PDE Surrogate Modeling
Department of Mechanical Engineering Johns Hopkins University Baltimore, MD 21218
AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow
Apr 13, 2026Jiale Liu, Nanzhe WangNeural Surrogate Modeling PDE Surrogate Modeling
School of Physics and Astronomy, The University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom · Institute of GeoEnergy Engineering, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom · Subsurface Energy Transition and Innovation Centre, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom
One Scale at a Time: Scale-Autoregressive Modeling for Fluid Flow Distributions
Apr 13, 2026Mario Lino, Nils ThuereyGenerative Modeling Autoregressive Modeling
Technical University of Munich
Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction
Apr 13, 2026Víctor Francés-Belda, Carlos Sanmiguel Vila, Rodrigo CastellanosConditional Diffusion Models Computational Fluid Dynamics
Theoretical and Computational Aerodynamics Group Flight Physics Department. Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA) · Department of Aerospace Engineering Universidad Carlos III de Madrid Madrid, Spain · Aerial Platforms Department Subdirectorate General for Aeronautical Systems Spanish National Institute for Aerospace Technology (INTA)
Physics-guided surrogate learning enables zero-shot control of turbulent wings
Apr 10, 2026Yuning Wang, Pol Suarez, Mathis Bode +1RL Control Computational Fluid Dynamics
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA. · FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden. · Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
Approximation of the Basset force in the Maxey-Riley-Gatignol equations via universal differential equations
Apr 9, 2026Finn Sommer, Vamika Rathi, Sebastian Goetschel +1Dynamical Systems Computational Fluid Dynamics
Chair Computational Mathematics, Institute of Mathematics, Hamburg University of Technology, 21073 Hamburg, Germany
Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
Apr 9, 2026Tong Duy Son, Kohta Sugiura, Marc Brughmans +7Graph Representation Learning Physics-Informed GNNs
The authors are with Siemens Digital Industries Software, Leuven, Belgium.
A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
Apr 8, 2026Elsa Cardoso-Bihlo, Alex BihloDifferentiable Physics Computational Fluid Dynamics
Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, NL, A1C 5S7, Canada
Calibration of a neural network ocean closure for improved mean state and variability
Apr 7, 2026Pavel Perezhogin, Alistair Adcroft, Laure ZannaModel Calibration Parameter Estimation
Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY, USA · Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA

Apr 30, 2026J/K move · Enter open · S save

Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining

Manho Park, Christopher V. Rackauckas, Christopher W. Tessum

The Grainger College of Engineering, Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

PDE Surrogate Modeling Computational Fluid Dynamics

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Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or have sacrificed spatial resolution compared to the model they are trained to emulate. We developed a machine-learned solver that speeds up advection simulations without sacrificing spatial resolution through the use of temporal coarse-graining, where the model is trained to take larger integration steps than dictated by the Courant-Friedrich-Lewy (CFL) condition. Our solver framework includes a convolutional neural network that takes concentrations and CFL numbers as inputs and outputs mass flux. Our solvers emulate 10-day ground-level horizontal advection simulations with r

^2

values against the baseline ranging from 0.60--0.98 with temporal coarsening factors of 4 to 32 times the baseline integration time step. Speed increases and accuracy decreases with increased coarsening, with

r^2 = 0.24

in accuracy lost for every factor of 10 gained in speed, reaching a maximum 92

\times

speedup while maintaining

r^2 = 0.60

. We deliberately trained our solvers only on January ground-level wind data to examine their ability to generalize across seasons and vertical heights. The 4

\times

-coarsened learned solver successfully reproduces simulations over 72 vertical levels. The 8

\times

--16

\times

solvers (but not 32

\times

) emulate most vertical levels. The learned solvers also generalize well across seasons, except for instabilities in June and October. With additional fine-tuning, these learned solvers could be appropriate for operational use where trading accuracy for speed could be advantageous, such as in screening tools, in ensemble simulations, or with data assimilation.