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.

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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
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
Addressing A Posteriori Performance Degradation in Neural Network Subgrid Stress Models
Nov 21, 2025Andy Wu, Sanjiva K. LeleNeural Network Generalization Data Augmentation
Department of Aeronautics and Astronautics, Stanford University, Stanford, California, USA · Department of Mechanical Engineering, Stanford University, Stanford, California, USA
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Nov 19, 2025Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22Physics-Based Simulation Computational Fluid Dynamics
Polymathic AI · Flatiron Institute · New York University +9
RheOFormer: A generative transformer model for simulation of complex fluids and flows
Oct 1, 2025Maedeh Saberi, Amir Barati Farimani, Safa JamaliNeural Surrogate Modeling Transformer
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA · Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
Sep 19, 2025Simon Klaes, Axel Klawonn, Natalie Kubicki +4PDE Solving PDE Surrogate Modeling
Department of Mathematics and Computer Science, University of Cologne, Germany · Center for Data and Simulation Science, University of Cologne, Germany · Institute for Advanced Simulation (IAS-2), Forschungszentrum Jülich, Germany +1
Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence
Jul 1, 2025Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. LermusiauxDiffusion Model Guidance Data Assimilation
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA · Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, USA
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
May 30, 2025Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades +8Diffusion-Based Inverse Problems Inverse Problems
Department of Aerospace Engineering, University of Michigan, Ann Arbor, USA · FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden · Division of Robotics, Perception and Learning, School of EECS, KTH Royal Institute of Technology, Stockholm, Sweden +7
AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD
May 19, 2025Xigui Li, Yuanye Zhou, Feiyang Xiao +16Neural Surrogate Modeling Synthetic Data Pretraining
Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China · Shanghai Academy of Artificial Intelligence for Science, Shanghai, China · Human Phenome Institute, Fudan University, Shanghai, China +1
Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
Date pendingFynn Fromme, Hans Harder, Christine Allen-Blanchette +1Autoencoders PDE Surrogate Modeling
Lamarr Institute for Machine Learning and Artificial Intelligence and Karlsruhe Institute of Technology · Lamarr Institute for Machine Learning and Artificial Intelligence and Department of Computer Science at Paderborn University · Department of Mechanical and Aerospace Engineering Center for Statistics and Machine Learning Princeton University +1

Feb 25, 2026, cs.LGJ/K move · Enter open · S save

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke, Aniket C. Joshi+3

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

Surrogate Modeling PDE Surrogate Modeling Reduced-Order Modeling Computational Fluid Dynamics

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Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces. Conventional high-fidelity numerical models require a high resolution to obtain reliable results, resulting in huge computational expense. This restricts the applicability of these models for multi-query problems, such as uncertainty quantification and optimisation, which require running numerous scenarios. As a cheaper alternative to high-fidelity models, this work develops eight surrogate models for predicting the fluid flow in porous media. Four of these are reduced-order models (ROM) based on one neural network for compression and another for prediction. The other four are single neural networks with the property of grid-size invariance; a term which we use to refer to image-to-image models that are capable of inferring on computational domains that are larger than those used during training. In addition to the novel grid-size-invariant framework for surrogate models, we compare the predictive performance of UNet and UNet++ architectures, and demonstrate that UNet++ outperforms UNet for surrogate models. Furthermore, we show that the grid-size-invariant approach is a reliable way to reduce memory consumption during training, resulting in good correlation between predicted and ground-truth values and outperforming the ROMs analysed. The application analysed is particularly challenging because fluid-induced rock dissolution results in a non-static solid field and, consequently, it cannot be used to help in adjustments of the future prediction.

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