:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Sharpe, Peter
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Fluid Dynamics
Online Access:	https://arxiv.org/abs/2511.15856
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
by: Huang, Yunfei, et al.
Published: (2025)

Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
by: Fromme, Fynn, et al.
Published: (2025)

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
by: Sharpe, Peter, et al.
Published: (2025)

Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
by: Poels, Yoeri, et al.
Published: (2024)

Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers
by: Choudhary, Naman, et al.
Published: (2025)

Accelerating Transient CFD through Machine Learning-Based Flow Initialization
by: Sharpe, Peter, et al.
Published: (2025)

Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks
by: Kurz, Marius, et al.
Published: (2025)

Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method
by: Marlantes, Kyle E., et al.
Published: (2024)

Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD
by: Setinek, Paul, et al.
Published: (2025)

Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection
by: Markmann, Thorben, et al.
Published: (2024)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
by: Raut, Riddhiman, et al.
Published: (2025)

Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Prediction
by: Francés-Belda, Víctor, et al.
Published: (2026)

Online learning to accelerate nonlinear PDE solvers: applied to multiphase porous media flow
by: Silva, Vinicius L S, et al.
Published: (2025)

GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
by: Shiratori, Suguru, et al.
Published: (2025)

Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
by: Mondal, Sudeepta, et al.
Published: (2026)

A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Field
by: Sarker, Apurba, et al.
Published: (2025)

Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
by: Shende, Takshak, et al.
Published: (2026)

Generalizable data-driven turbulence closure modeling on unstructured grids with differentiable physics
by: Kim, Hojin, et al.
Published: (2023)

Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
by: Jnini, Anas, et al.
Published: (2025)

Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
by: Lee, Minseo, et al.
Published: (2026)

LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics
by: Xiao, Qisong, et al.
Published: (2026)

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
by: Ashton, Neil, et al.
Published: (2026)

Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model
by: Xue, Xiao, et al.
Published: (2025)

Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves
by: Cai, Shukai, et al.
Published: (2026)

A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
by: Gao, Rui, et al.
Published: (2022)

BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements
by: Veremchuk, Maksym, et al.
Published: (2026)

Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks
by: Ortali, Giulio, et al.
Published: (2024)

Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation
by: Yilmaz, Emre, et al.
Published: (2025)

Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition
by: Deo, Indu Kant, et al.
Published: (2025)

Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
by: Barwey, Shivam, et al.
Published: (2023)

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
by: Pinheiro, Nathalie C., et al.
Published: (2026)

Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning
by: Straat, Michiel, et al.
Published: (2025)

PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
by: Liu, Yuxuan, et al.
Published: (2024)

A Multi-fidelity Double-Delta Wing Dataset and Empirical Scaling Laws for GNN-based Aerodynamic Field Surrogate
by: Shen, Yiren, et al.
Published: (2025)

Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls
by: Vardhan, Harsh, et al.
Published: (2023)

Predicting Flow Dynamics using Diffusion Models
by: Gachnang, Yannick, et al.
Published: (2025)

Analysis of tidal flows through the Strait of Gibraltar using Dynamic Mode Decomposition
by: Dias, Sathsara, et al.
Published: (2023)

Flexible SE(2) graph neural networks with applications to PDE surrogates
by: Bånkestad, Maria, et al.
Published: (2024)

Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
by: Straat, Michiel, et al.
Published: (2025)

Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality
by: Lee, Sangjoon, et al.
Published: (2025)