:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Yihao, Yang, Yue
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Fluid Dynamics Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2409.08815
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Deep reinforcement learning based navigation of a jellyfish-like swimmer in flows with obstacles
di: Chen, Yihao, et al.
Pubblicazione: (2025)

Measurements and modeling of swimming speed dependence on stroke frequency in scyphozoan jellyfish
di: Yoder, Noa K., et al.
Pubblicazione: (2026)

How do trout regulate patterns of muscle contraction to optimize propulsive efficiency during steady swimming
di: Li, Tao, et al.
Pubblicazione: (2025)

From wake dynamics to energy consumption in free-swimming biohybrid robotic jellyfish: a multiscale analysis
di: Anuszczyk, Simon R., et al.
Pubblicazione: (2026)

Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
di: Tonti, Federica, et al.
Pubblicazione: (2026)

Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
di: Liu, Yunfei, et al.
Pubblicazione: (2026)

LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
di: Li, Yue
Pubblicazione: (2025)

Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling
di: Yang, Sunwoong, et al.
Pubblicazione: (2025)

Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations
di: Li, Yue, et al.
Pubblicazione: (2025)

Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
di: Tonti, Federica, et al.
Pubblicazione: (2025)

Pi-fusion: Physics-informed diffusion model for learning fluid dynamics
di: Qiu, Jing, et al.
Pubblicazione: (2024)

Physics-guided surrogate learning enables zero-shot control of turbulent wings
di: Wang, Yuning, et al.
Pubblicazione: (2026)

Identifying regions of importance in wall-bounded turbulence through explainable deep learning
di: Cremades, Andres, et al.
Pubblicazione: (2023)

Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
di: Dong, Zhehao, et al.
Pubblicazione: (2025)

Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics
di: Sandubete-López, Juan, et al.
Pubblicazione: (2024)

High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
di: Montalà, Ricard, et al.
Pubblicazione: (2026)

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning
di: Abadía-Heredia, Rodrigo, et al.
Pubblicazione: (2025)

A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application
di: Lagemann, Esther, et al.
Pubblicazione: (2024)

LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements
di: Hetherington, Ashton, et al.
Pubblicazione: (2024)

AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
di: Somasekharan, Nithin, et al.
Pubblicazione: (2026)

CFD-copilot: leveraging domain-adapted large language model and model context protocol to enhance simulation automation
di: Dong, Zhehao, et al.
Pubblicazione: (2025)

Implicit factorized transformer approach to fast prediction of turbulent channel flows
di: Yang, Huiyu, et al.
Pubblicazione: (2024)

A neural operator framework for data-driven discovery of stability and receptivity in physical systems
di: Wang, Chengyun, et al.
Pubblicazione: (2026)

Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
di: Chen, Shengyu, et al.
Pubblicazione: (2024)

MetaOpenFOAM: an LLM-based multi-agent framework for CFD
di: Chen, Yuxuan, et al.
Pubblicazione: (2024)

OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD
di: Chen, Yuxuan, et al.
Pubblicazione: (2025)

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
di: Shan, Siming, et al.
Pubblicazione: (2024)

Thin ring wing as a means of flow improvement upstream of a propeller
di: Sluchak, Vladimir
Pubblicazione: (2025)

Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer
di: Cremades, Andrés, et al.
Pubblicazione: (2024)

Electromechanical enhancement of live jellyfish for ocean exploration
di: Anuszczyk, Simon R., et al.
Pubblicazione: (2023)

Deep Reinforcement Learning in Action: Real-Time Control of Vortex-Induced Vibrations
di: Sababha, Hussam, et al.
Pubblicazione: (2025)

LLM Agent for Fire Dynamics Simulations
di: Xu, Leidong, et al.
Pubblicazione: (2024)

Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method
di: Wang, Xutun, et al.
Pubblicazione: (2024)

The Principle of Minimum Pressure Gradient: An Alternative Basis for Physics-Informed Learning of Incompressible Fluid Mechanics
di: Alhussein, Hussam, et al.
Pubblicazione: (2024)

Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics
di: Feng, Haodong, et al.
Pubblicazione: (2025)

Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control
di: Vaillant, Mikael, et al.
Pubblicazione: (2025)

Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
di: Vishwasrao, Abhijeet, et al.
Pubblicazione: (2025)

Revealing Low-Dimensional Structure in 2D Richtmyer-Meshkov Instabilities via Parametric Reduced-Order Modeling
di: Messenger, Daniel, et al.
Pubblicazione: (2025)

PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
di: Radler, Andreas, et al.
Pubblicazione: (2025)

PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
di: Jiang, Liang, et al.
Pubblicazione: (2025)