Computational Fluid Dynamics

Also known as CFD

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23 papers in the last four weeks, up 667% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 165

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A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling
Jun 23, 2026Somyajit Chakraborty, Ming Pan, Xizhong ChenSurrogate Modeling PDE Surrogate Modeling
Shanghai Jiao Tong University, Department of Chemistry and Chemical Engineering 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
Neural Network-Based Parametric Model Reduction for Predicting Turbulent Flow for Different Vehicle Geometries
Jun 23, 2026Kazuto Ando, Rahul Bale, Akiyoshi Kuroda +1Reduced-Order Modeling Computational Fluid Dynamics
Operations and Computer Technologies Division, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan · Complex Phenomena Unified Simulation Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo 650-0047, Japan · Department of Computational Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai-cho, Nada-ku, Kobe, 657-8501, Hyogo, Japan
Prediction of Viscoelastic Droplet Impact Dynamics Using a Vision Transformer-Based Approach
Jun 22, 2026Diego A. de Aguiar, Cassio M. OishiPDE Surrogate Modeling Video Prediction
Departamento de Matem´atica e Computa¸c˜ao, Faculdade de Ciˆencias e Tecnologia, Universidade Estadual Paulista “J´ulio de Mesquita Filho”, Presidente Prudente, Brazil
Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids
Jun 22, 2026Federico Lanteri, Massimiliano CremonesiNeural Surrogate Modeling Self-Attention
Department of Civil and Environmental Engineering, Politecnico di Milano Piazza Leonardo da Vinci, 32, 20133, Milano, Italy
Patched Flow Matching: Generative Wall-Pressure Reconstruction Beyond Training-Domain Scales from Sparse Sensors
Jun 20, 2026Meet Hemant Parikh, Yi Liu, Jian-Xun WangFlow Matching Generative Modeling
aSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY · bDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN
Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence
Jun 19, 2026Yilong Dai, Yiming Sun, Yiheng Chen +4Variational Autoencoders Physics-Informed ML
The University of Alabama · University of Pittsburgh · University of Maryland, College Park
Structure-Oriented Randomized Neural Networks for Poisson-Nernst-Planck and Poisson-Nernst-Planck-Navier-Stokes Systems
Jun 18, 2026Yunlong Li, Fei WangNeural PDE Solvers Computational Fluid Dynamics
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.
Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Jun 17, 2026Gabriel F. Barros, Rômulo M. Silva, Alvaro L. G. A. CoutinhoPDE Surrogate Modeling Reduced-Order Modeling
COPPE −Federal University of Rio de Janeiro - UFRJ, Rio de Janeiro, Brazil
Acceleration of an algebraic multigrid pressure solver using graph neural networks
Jun 17, 2026Eric Chillón, Artur K. Lidtke, Nguyen Anh Khoa Doan +1Graph Neural Networks PDE Solving
Faculty of Mechanical Engineering, Delft University of Technology, The Netherlands · Maritime Research Institute Netherlands, The Netherlands · Department of Aeronautics, Imperial College London, United Kingdom
A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows
Jun 15, 2026Lukas Schröder, Shubham Kavane, Harald KöstlerNeural Surrogate Modeling Synthetic Benchmark Generation
Chair of Computer Science 10 (System Simulation) Friedrich-Alexander-Universität Erlangen-Nürnberg
Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation
Jun 15, 2026Julius H Ramlau, Friedrich Hastedt, Tolga Birdal +3PDE Surrogate Modeling Adaptive Mesh Refinement
Department of Chemical Engineering, Imperial College London, London, United Kingdom · CMAC, CEDAR, an EPSRC Center for Doctoral Training in Cyber-Physical Systems for Medicines Development and Manufacturing, Imperial College London, London, United Kingdom · Sargent Centre for Process Systems Engineering, Imperial College London, London, United Kingdom +2
ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics
Jun 13, 2026Kirsten Odendaal, George DrakoulasNeural Surrogate Modeling Computational Fluid Dynamics
Maritime Research Insitute Netherlands Wageningen, Netherlands · Damen Research Gorinchem, Netherlands
Multiscale Hypersonic Boundary Layer Reconstruction via Spectral Binning and Subdomain-wise Conditional Diffusion
Jun 12, 2026Hojin Kim, Dibyajyoti Chakraborty, Takahiko Toki +2Neural Surrogate Modeling Conditional Diffusion Models
School of Mechanical Engineering, Purdue University, West Lafayette, 47907, IN, USA · College of Information Sciences and Technology, Pennsylvania State University, University Park, 16802, PA, USA · Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, 60439, IL, USA
A fully GPU-based workflow for building physics emulators of hypersonic flows
Jun 11, 2026Fabian Paischer, Dylan Rubini, Deniz A. Bezgin +6Neural Surrogate Modeling Physics-Informed ML
ELLIS Unit, Institute for Machine Learning, JKU Linz · EMMI AI, Linz · Chair of Aerodynamics and Fluid Mechanics, TU Munich, Germany
Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
Jun 10, 2026Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1Kalman Filtering Data Assimilation
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
Spectrally Regularized Latent Flow Matching for Turbulence Generation
Jun 10, 2026Khalid Rafiq, Aditya G. NairSpectral Regularization Flow Matching
Department of Mechanical Engineering University of Nevada, Reno, NV, USA
Sparse probes and murky physics: a case study of interpretability challenges in a foundation model for continuum dynamics
Jun 10, 2026Katherine Rosenfeld, Maike SonnewaldNeural Surrogate Modeling Mechanistic Interpretability
Gates Foundation · UC Davis
Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow
Jun 8, 2026Daniel Cieslak, Andrzej CzyzewskiPDE Surrogate Modeling Physics-Informed ML
Gdańsk University of Technology, Gdańsk, Poland
Geometry-Aware Anisotropic Boundary Correction for Aerodynamic Simulation
Jun 8, 2026Xin Zhang, Yipeng Huang, Shu Jiang +2Neural Operators Computational Fluid Dynamics
School of Informatics, Xiamen University, Xiamen, China · Institute of Artificial Intelligence, Xiamen University, Xiamen, China
Drifting Models for Surrogate Flow Modeling
Jun 5, 2026Chris R. Jung, Markus Dörr, Natalie Jüngling +3PDE Surrogate Modeling Conditional Generative Modeling
Center for Machine Learning (ZML) 2Institute for Flow in Additively Manufactured Porous Structures (ISAPS) Heilbronn University of Applied Sciences, 74081 Heilbronn, Germany
A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction
Jun 5, 2026Kangkang Qi, Huiyu Yang, Keqi Ding +5PDE Surrogate Modeling Computational Fluid Dynamics
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 · Shenzhen Tenfong Technology Co., Ltd., Shenzhen 518000, China +1
Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Prediction
Jun 4, 2026Edward Holmberg, Elias Ioup, Md Meftahul Ferdaus +2Neural Surrogate Modeling PDE Surrogate Modeling
Canizaro Livingston Gulf States Center for Environmental Informatics, Department of Computer Science, The University of New Orleans, New Orleans, LA 70148, USA · Center for Geospatial Sciences, Naval Research Laboratory, Mississippi, USA · Ocean Sciences Division, Naval Research Laboratory, Mississippi, USA
Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
Jun 4, 2026Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza +1Differentiable Physics Physics-Informed ML
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA · Department of Mechanical Engineering, University of Wisconsin–Milwaukee, Milwaukee, WI, USA +1
Reward hacking in physical reinforcement learning revealed by turbulent drag reduction
Jun 4, 2026Giorgio Maria Cavallazzi, Miguel Pérez-Cuadrado, Alfredo PinelliRL Control Reward Hacking
School of Science and Technology, Department of Engineering, City St. George’s, University of London, London, UK
Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame
Jun 1, 2026Nicolas J. Tricard, Benjamin C. Koenig, Sili DengSurrogate Modeling Computational Fluid Dynamics
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
May 31, 2026Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20Sim-to-Real Transfer Physics-Based Simulation
University of Cambridge, UK · Polymathic AI · CEA, DAM/DIF, 91297 Arpajon Cedex, France +6
Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
May 31, 2026Federica Tonti, Ricardo VinuesaRL Control Reinforcement Learning
Department of Aerospace Engineering University of Michigan Ann Arbor, MI 48109, United States
Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs
May 31, 2026Lennon J. Shikhman, Shane GilbertiePDE Surrogate Modeling Scientific ML
College of Computing, Georgia Institute of Technology, Atlanta, 30332, GA, USA · Department of Aerospace, Physics and Space Sciences, Florida Institute of Technology, Melbourne, 32901, FL, USA
(HB-ARFM) History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction
May 29, 2026Xianwei Zou, Sheikh Md Shakeel Hassan, Arthur Feeney +1Flow Matching PDE Inverse Problems
Department of Electrical Engineering and Computer Science, University of California Irvine, USA.
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
May 28, 2026Minseo Lee, Seongmin Oh, Chaehyeon Song +5PDE Surrogate Modeling Reduced-Order Modeling
Division of Advanced Nuclear Engineering, POSTECH, 772026 Cheongam-Ro, Pohang-si, 37673, Gyeongbuk-do, Republic of Korea · Department of Quantum System Engineering, Jeonbuk National University, 567 Baekje-daero, Jeonju-si, 54896, Jeollabuk-do, Republic of Korea · Department of Nuclear Engineering, Hanyang University, Wangsimni-ro, Seongdong-gu, Seongdong-gu, 04763, Seoul, Republic of Korea +1

Jun 23, 2026, physics.flu-dynJ/K move · Enter open · S save

A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling

Somyajit Chakraborty, Ming Pan, Xizhong Chen

Shanghai Jiao Tong University, Department of Chemistry and Chemical Engineering 800 Dongchuan Road, Minhang District, Shanghai, 200240, China

Surrogate Modeling PDE Surrogate Modeling Wavelet Methods Physics-Informed ML Computational Fluid Dynamics

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Physics-informed surrogate models can accelerate computational fluid dynamics simulations. However, many existing methods reproduce global flow patterns more reliably than localized multiscale structures. This study presents a physics-informed Fourier-wavelet transformer for next-step velocity-field reconstruction in real-world flow benchmarks. The proposed formulation combines hybrid Fourier-wavelet spectral encoding with physics-biased self-attention based on partial differential equation residual diagnostics. It also uses self-supervised pretraining through Masked Physics Prediction and Equation Consistency Prediction. The experiments are conducted on two real benchmark cases: cylinder-wake flow and fluid-structure interaction. All approaches are evaluated under a shared local protocol and compared with spectral, transformer-based, operator-learning, and physics-informed neural-network baselines. On the cylinder-wake benchmark, the proposed model achieves the best aggregate accuracy, with an all-channel normalized mean-squared error of 0.05875 and an all-channel Pearson correlation coefficient of 0.97019. On the fluid-structure-interaction benchmark, it gives the lowest all-channel normalized mean-squared error of

2.70 \times 10^{-4}

, compared with

4.02 \times 10^{-4}

for the strongest baseline. Component-wise field comparisons and scale-separated diagnostics further show stronger recovery of localized wake structures, including near-body, wake-core, and far-wake features. The results demonstrate improved real-world flow reconstruction while maintaining a practical accuracy-cost tradeoff.

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