Computational Fluid Dynamics

Also known as CFD

Follow this topic Ask about this topic

Momentum

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

All topics

AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
May 7, 2026Francisco Giral, Abhijeet Vishwasrao, Andrea Arroyo Ramo +8Joint-Embedding Predictive Architecture PDE Surrogate Modeling
Universidad Politécnica de Madrid · University of Michigan · Universitat Politècnica de València +3
Towards Scalable One-Step Generative Modeling for Autoregressive Dynamical System Forecasting
May 7, 2026Tianyue Yang, Xiao XuePDE Surrogate Modeling One-Step Generative Modeling
The Center for Computational Science University College London
Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
May 6, 2026Saghar Zolfaghari, Yu Xie, Junfeng Yang +1Neural Surrogate Modeling Multifidelity Modeling
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA · School of Mechanical Engineering, University of Leeds, Leeds, UK
Deep Wave Network for Modeling Multi-Scale Physical Dynamics
May 5, 2026Alexander I. Khrabry, Edward A. Startsev, Andrew T. Powis +1U-Net Multi-Scale Feature Fusion
Princeton University, Princeton, New Jersey 08544, USA · FusionSoft LLC, Lawrenceville, New Jersey 08648, USA · Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540, USA
Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
May 5, 2026Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1Neural Surrogate Modeling Implicit Neural Representations
Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology, Am Schwarzenberg-Campus 4, D-21073 Hamburg, Germany · Institute of Mathematics, Hamburg University of Technology, Am Schwarzenberg-Campus 3, D-21073 Hamburg, Germany
Mesh Based Simulations with Spatial and Temporal awareness
May 2, 2026Paul Garnier, Vincent Lannelongue, Elie HachemGraph Neural Networks Neural PDE Solvers
1CEMEF - Mines Paris PSL
An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
May 1, 2026Shihang Zhao, Martín Saravia, Haokui Jiang +2PDE Surrogate Modeling Physics-Based Simulation
Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, 518055, China · Centro de Investigación en Mecánica Teórica y Aplicada, Universidad Tecnológica Nacional - CONICET, 11 de Abril 461, Bahía Blanca, Argentina
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 PDE Operator Learning
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 +3Fourier Neural Operator PDE Surrogate Modeling
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 PDE Solving
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 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
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
Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
Mar 22, 2026Janne Perini, Rafael Bischof, Moab Arar +4Video Diffusion Models PDE Surrogate Modeling
1Computational Design Lab, ETH Zurich, Switzerland · 2Tel Aviv University, Israel · 3Architecture and Building Systems, ETH Zurich, Switzerland +2
An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
Mar 20, 2026V. S. Maduri, K. B. NakshatralaNeural PDE Solvers Physics-Informed ML
Department of Civil & Environmental Engineering University of Houston, Houston, Texas 77204, USA.
A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames
Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4Autoencoders PDE 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
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
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

May 7, 2026, cs.LGJ/K move · Enter open · S save

AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling

Francisco Giral, Abhijeet Vishwasrao, Andrea Arroyo Ramo, Mahmoud Golestanian+7

Universidad Politécnica de Madrid · University of Michigan · Universitat Politècnica de València+3

Joint-Embedding Predictive Architecture PDE Surrogate Modeling Predictive Representation Learning Computational Fluid Dynamics

Open paper AskPDF arXiv

High-fidelity CFD is essential for aerodynamic design, but repeated simulations are computationally expensive, motivating surrogate models for rapid evaluation across geometries and operating conditions. Most existing surrogates are designed for direct field regression, requiring the evaluation of millions of field points even when only an aerodynamic quantity or a localized region is needed, while their internal representations are not intended for direct use in downstream tasks. We introduce AeroJEPA, a framework inspired by joint-embedding predictive architectures that represents the problem in two distinct latent spaces: context tokens encode geometry, while predicted tokens encode the aerodynamic state. Both representations remain directly accessible for downstream tasks, such as linear readouts of design variables and aerodynamic quantities without decoding and integrating the full field. When spatial detail is needed, a continuous implicit decoder evaluates the field only at the requested coordinates while reusing the encoded geometry. We evaluate AeroJEPA on HiLiftAeroML, with multi-million-point fields, and SuperWing, which spans a broad family of transonic wings. Compared with state-of-the-art direct-regression surrogates, AeroJEPA trades peak full-field accuracy for compact, reusable representations. In our selective-decoding experiment, however, AeroJEPA substantially outperforms the evaluated direct-regression surrogates while avoiding predictions over the remainder of the aircraft. The learned representations further support controlled interpolation, concept-vector arithmetic, and preliminary constrained latent-space optimization. These results show how predictive representations can support aerodynamic analysis with or without full-field reconstruction.

Momentum

Related topics

Latest papers 165