Computational Fluid Dynamics

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

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Striding Across Reynolds Numbers: Representation Geometry in Neural PDE Generalisation
May 28, 2026Jianing ShiNeural PDE Solvers Neural Representation Geometry
London School of Economics and Political Science.
Sequential Physics-Constrained Neural Operator Forward Modeling for the $\textit{Norne}$ Reservoir System
May 27, 2026Clement Etienam, Juntao Yang, Oleg Ovcharenko +4Fourier Neural Operator PDE Surrogate Modeling
NVIDIA Corporation
History-aware adaptive reduced-order models via incremental singular value decomposition
May 27, 2026Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano +2Reduced-Order Modeling Singular Value Decomposition
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA · Department of Aerospace Engineering, University of Kansas, Lawrence, KS, USA · Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
May 27, 2026Seunghwan Keum, Alok WareyPDE Surrogate Modeling Low-Rank Adaptation
General Motors Research and Development
Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks
May 26, 2026Tomoki Koike, Prakash Mohan, Marc T. Henry de Frahan +2Manifold Learning Feature Selection
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA · Computational Science Center, National Laboratory of the Rockies (NLR), Golden, CO
CFDTwin: An open-source GUI and Python toolkit for POD-NN surrogate modeling of ANSYS Fluent simulations
May 26, 2026Daniel Curl, Han HuPDE Surrogate Modeling Proper Orthogonal Decomposition
Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
May 26, 2026Yunfei Liu, Hao Wang, Yuhang Qi +8Hierarchical Representation Learning Computational Fluid Dynamics
Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000,China. · Laboratory of Digitizing Software for Frontier Equipment,National University of Defense Technology,Changsha,410073,China. · National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha,410073,China. +1
Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
May 25, 2026Daniel Dehtyriov, Jonathan F. MacArt, Justin SirignanoPhysics-Informed ML Computational Fluid Dynamics
Mathematical Institute University of Oxford Oxford OX2 6GG, United Kingdom · Aerospace and Mechanical Engineering University of Notre Dame Notre Dame, IN 46556, USA
Accelerating Bayesian inverse design in computational fluid dynamics using neural operators
May 25, 2026Bipin Tiwari, Omer SanNeural Surrogate Modeling Bayesian Inverse Problems
Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville,2026 TN 37996, USA
Samudra 2: Scaling Ocean Emulators across Resolutions
May 24, 2026Yuan Yuan, Jesse Rusak, Alexander Merose +5Neural Surrogate Modeling Climate Science
Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY, USA · Open Athena AI Foundation, Inc., New York, NY, USA · Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
May 23, 2026Logan A. Burnett, Hyungjun Kim, Hsien-Cheng Chou +5Neural Surrogate Modeling Spatiotemporal Forecasting
Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States · Korea Atomic Energy Research Institute, Daejeon, 34057, South Korea · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States +2
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
May 22, 2026Qian Zhang, George Em KarniadakisPDE Operator Learning Operator Learning
Division of Applied Mathematics, Brown University
ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
May 21, 2026Chengze Li, Lingwei Wei, Li Sun +7PDE Surrogate Modeling Computational Fluid Dynamics
University of Illinois Chicago · 2Beijing University of Posts and Telecommunications · 3North China Electric Power University
Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
May 19, 2026Takshak Shende, Viktor PopovPDE Surrogate Modeling Physics-Informed ML
Department of Mechanical Engineering, University College London (UCL), London, United Kingdom · Ascend Technologies Ltd, Southampton, United Kingdom
HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
May 19, 2026Neil Ashton, Adam Clark, Liam Heidt +11Computational Fluid Dynamics
NVIDIA, Santa Clara, US · The Boeing Company, Seattle, US · Cadence Design Systems, Santa Clara, US
An End-to-End PyTorch Interface for Differentiable PDE Solvers: A RANS Model-Correction Study
May 19, 2026Luca Saverio, Michele Alessandro Bucci, Gianmarco Farro +2PDE Solving Differentiable Optimization
MONHADE · Digital Sciences & Technologies Department, Safran Tech, Magny-Les-Hameaux, 78114, France. · DAAA, ONERA, Institut Polytechnique de Paris, Meudon, 92190, France. +1
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
May 18, 2026Bipin Tiwari, Muhammad Abid, Omer SanNeural Surrogate Modeling Uncertainty Quantification
Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville Knoxville, TN 37996, USA.
Physics-informed convolutional neural networks for fluid flow through porous media
May 18, 2026Rafał Topolnicki, Paweł Dłotko, Maciej MatykaPDE Surrogate Modeling Physics-Informed ML
Dioscuri Center in Topological Data Analysis, Institute of Mathematics, Polish Academy of Sciences, ul. ´Sniadeckich 8, 00-656, Warsaw, Poland · Institute of Experimental Physics, Faculty of Physics and Astronomy, University of Wrocław, pl. M. Borna 9, 50-204, Wrocław, Poland · Institute of Theoretical Physics, Faculty of Physics and Astronomy, University of Wrocław, pl. M. Borna 9, Wrocław, 50-204, Poland +1
Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
May 18, 2026Bo Chen, Yitong Fan, Jie Yao +1Long-Term Time Series Forecasting Hybrid CNN-Transformer Architectures
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China · School of Interdisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
Wavelet Flow Matching for Multi-Scale Physics Emulation
May 15, 2026Gabriele Accarino, Juan Nathaniel, Carla Roesch +4Flow Matching PDE Surrogate Modeling
Department of Earth and Environmental Engineering Columbia University, NY, USA · University of Edinburgh Edinburgh, Scotland, UK · Courant Institute of Mathematical Sciences New York University, NY, USA +2
FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives
May 15, 2026Huaxi Huang, Meng Li, Zhengqing Gao +3Neural Surrogate Modeling Gaussian Splatting
Shanghai Artificial Intelligence Laboratory · The Hong Kong University of Science and Technology · Mohamed bin Zayed University of Artificial Intelligence +1
When Does Equivariance Help? Canonical Alignment in Neural Fluid Surrogates
May 12, 2026Patryk Rygiel, Julian Suk, Kak Khee Yeung +2Neural Surrogate Modeling Equivariant Neural Networks
Department of Applied Mathematics Technical Medical Centre Cardiovascular Health Technology Centre University of Twente · Department of Computer Science Munich Center for Machine Learning Technical University of Munich · Department of Surgery Amsterdam UMC, Location University of Amsterdam Atherosclerosis & Aortic diseases Amsterdam Cardiovascular Sciences
Compositional Neural Operators for Multi-Dimensional Fluid Dynamics
May 12, 2026Hamda Hmida, Hsiu-Wen Chang, Youssef MesriPDE Operator Learning Physics-Informed Neural Operators
Mines Paris - PSL University, Centre for Material Forming (CEMEF) · Mines Paris - PSL University, Centre for Robotics (CAOR)
The finite expression method for turbulent dynamics with high-order moment recovery
May 11, 2026Xingjian Xu, Di Qi, Chunmei WangNonlinear System Identification Computational Fluid Dynamics
Department of Mathematics, University of Florida, Gainesvile, FL 32611 · Department of Mathematics, Purdue University, West Lafayette, IN 47907
LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction
May 10, 2026Ningxiao Tao, Baoquan Chen, Mengyu Chu3D Gaussian Splatting Sparse-View 3D Reconstruction
School of Intelligence Science and Technology, Peking University, China · Peking University, China · State Key Laboratory of General Artificial Intelligence, Peking University, China
Finite Volume-Informed Neural Network Framework for 2D Shallow Water Equations: Rugged Loss Landscapes and the Importance of Data Guidance
May 9, 2026Xiaofeng LiuPDE Solving PDE Surrogate Modeling
Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA · Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA
Inpainting physics: self-supervised learning for context-driven fluid simulation
May 9, 2026Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert +2Neural Surrogate Modeling Self-Supervised Learning
AI for Image-Guided Diagnosis and Therapy, Technical University of Munich · Munich Center for Machine Learning (MCML) · AI in Healthcare and Medicine, Technical University of Munich +1
Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning
May 8, 2026Mahdi Naderibeni, Liang Wu, David M. J. TaxPDE Surrogate Modeling Physics-Informed ML
aPattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, the Netherlands · bScience & Research and Innovation, dsm-firmenich, Delft, the Netherlands
Exactness Matters for Physical Rule Enforcement
May 8, 2026Bum Jun KimTime Series Forecasting Spatiotemporal Forecasting
The University of Tokyo, Japan
AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
May 7, 2026Nithin Somasekharan, Rabi Pathak, Manushri Dhanakoti +4AI Agents for Scientific Discovery Autonomous Scientific Discovery
1Rensselaer Polytechnic Institute

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Striding Across Reynolds Numbers: Representation Geometry in Neural PDE Generalisation

Jianing Shi

London School of Economics and Political Science.

Neural PDE Solvers Neural Representation Geometry Computational Fluid Dynamics

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Cross-Reynolds generalisation in neural PDE solvers remains poorly characterised. On the canonical forced 2D Navier-Stokes benchmark, a trained Fourier Neural Operator reaches 46.68% relative L2 error under a 10x Reynolds-number shift, yet zero-forward-model retrieval baselines already improve to 41-42%. This suggests representation geometry as a major organising variable among the tested methods. We test this hypothesis through ConvAE-Relay, which matches states in a source-trained convolutional autoencoder latent space and borrows dynamics from a source-regime database, achieving 38.34+/-0.07% using only a source-regime database and no target-regime fitting, labels, or database entries. A 2x2 ablation isolates matching quality as dominant over the update rule. Oracle experiments confirm that source-regime dynamics directions remain transferable (cosine similarity ~0.84) when matching stays on-manifold; autoregressive drift is the primary bottleneck (~12 percentage points). From the learned-prediction side, a U-Net with multi-scale skip connections achieves 34.72+/-0.60%, consistent with the retrieval-side finding that local, multi-scale representations organise cross-Reynolds transfer among tested methods. All claims are scoped to this benchmark.

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