:: Library Catalog

Buchumschlag

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Mitra, Peetak, Haghshenas, Majid, Santo, Niccolo Dal, Daly, Conor, Schmidt, David P.
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Fluid Dynamics Machine Learning
Online-Zugang:	https://arxiv.org/abs/2305.00114
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
von: Sengupta, Arindam, et al.
Veröffentlicht: (2025)

Comparison of CNN-based deep learning architectures for unsteady CFD acceleration on small datasets
von: Khanal, Sangam, et al.
Veröffentlicht: (2025)

Prospects of federated machine learning in fluid dynamics
von: San, Omer, et al.
Veröffentlicht: (2022)

Machine Learning Techniques for Data Reduction of CFD Applications
von: Lee, Jaemoon, et al.
Veröffentlicht: (2024)

Assessment of machine learning methods for state-to-state approaches
von: Campoli, Lorenzo, et al.
Veröffentlicht: (2021)

Benchmarking machine learning models for predicting aerofoil performance
von: Summerell, Oliver, et al.
Veröffentlicht: (2025)

Application of machine learning algorithm in temperature field reconstruction
von: He, Qianyu, et al.
Veröffentlicht: (2025)

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

On the locality of local neural operator in learning fluid dynamics
von: Ye, Ximeng, et al.
Veröffentlicht: (2023)

Reducing Spatial Discretization Error on Coarse CFD Simulations Using an OpenFOAM-Embedded Deep Learning Framework
von: Gonzalez-Sieiro, Jesus, et al.
Veröffentlicht: (2024)

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

Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
von: Shankar, Varun, et al.
Veröffentlicht: (2021)

Inpainting physics: self-supervised learning for context-driven fluid simulation
von: Weidner, Jonas, et al.
Veröffentlicht: (2026)

Method of data forward generation with partial differential equations for machine learning modeling in fluid mechanics
von: Chen, Ruilin
Veröffentlicht: (2025)

oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation
von: Dehtyriov, Daniel, et al.
Veröffentlicht: (2025)

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

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

A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
von: Sorensen, Benedikt Barthel, et al.
Veröffentlicht: (2024)

Improving aircraft performance using machine learning: a review
von: Clainche, Soledad Le, et al.
Veröffentlicht: (2022)

Loop2Net: Data-Driven Generation and Optimization of Airfoil CFD Meshes from Sparse Boundary Coordinates
von: Fan, Lushun, et al.
Veröffentlicht: (2025)

C(NN)FD -- a deep learning framework for turbomachinery CFD analysis
von: Bruni, Giuseppe, et al.
Veröffentlicht: (2023)

Single-snapshot machine learning for super-resolution of turbulence
von: Fukami, Kai, et al.
Veröffentlicht: (2024)

Hierarchy of extreme-event predictability in turbulence revealed by machine learning
von: Yang, Yuxuan, et al.
Veröffentlicht: (2026)

Virtual domain extension for imposing boundary conditions in flow simulation using pre-trained local neural operator
von: Ye, Ximeng, et al.
Veröffentlicht: (2025)

Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements
von: Kanin, E. A., et al.
Veröffentlicht: (2022)

XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
von: Baral, Shilaj, et al.
Veröffentlicht: (2025)

Neural network-based Godunov corrections for approximate Riemann solvers using bi-fidelity learning
von: Thakur, Akshay, et al.
Veröffentlicht: (2025)

Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
von: Fukami, Kai, et al.
Veröffentlicht: (2025)

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
von: Toshev, Artur P., et al.
Veröffentlicht: (2024)

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
von: McGreivy, Nick, et al.
Veröffentlicht: (2024)

Transfer learning strategies for accelerating reinforcement-learning-based flow control
von: Salehi, Saeed
Veröffentlicht: (2025)

Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows
von: Xiao, Ke, et al.
Veröffentlicht: (2026)

A Physics-informed machine learning model for time-dependent wave runup prediction
von: Naeini, Saeed Saviz, et al.
Veröffentlicht: (2024)

RheOFormer: A generative transformer model for simulation of complex fluids and flows
von: Saberi, Maedeh, et al.
Veröffentlicht: (2025)

Unsupervised simulation of incompressible flows with physics- and equality- constrained artificial neural networks
von: Hu, Qifeng, et al.
Veröffentlicht: (2025)

Advanced deep-reinforcement-learning methods for flow control: group-invariant and positional-encoding networks improve learning speed and quality
von: Jeon, Joongoo, et al.
Veröffentlicht: (2024)

Accelerating the discovery of steady-states of planetary interior dynamics with machine learning
von: Agarwal, Siddhant, et al.
Veröffentlicht: (2024)

An application of machine learning to the motion response prediction of floating assets
von: Morris-Thomas, Michael T. M. B., et al.
Veröffentlicht: (2025)

Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations
von: Catalani, Giovanni, et al.
Veröffentlicht: (2025)

Analysis of accuracy and efficiency of neural networks to simulate Navier-Stokes fluid flows with obstacles
von: Hespanha, Rui, et al.
Veröffentlicht: (2025)