Image de couverture de livre

LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Hetherington, Ashton, Leonés, Javier López, Clainche, Soledad Le
Format:	Preprint
Publié:	2024
Sujets:	Fluid Dynamics Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A low cost singular value decomposition based data assimilation technique for analysis of heterogeneous combustion data
par: Pillai, Prajith, et autres
Publié: (2025)

Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
par: Sengupta, Arindam, et autres
Publié: (2025)

Reduced-order modeling of a viscoelastic turbulent jet with hybrid machine learning models
par: Amor, Christian, et autres
Publié: (2026)

HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics
par: Barragán, Guillermo, et autres
Publié: (2025)

Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
par: Abadía-Heredia, Rodrigo, et autres
Publié: (2024)

MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
par: Barragán, Guillermo, et autres
Publié: (2025)

Data repairing and resolution enhancement using data-driven modal decomposition and deep learning
par: Hetherington, A., et autres
Publié: (2024)

Generative artificial intelligence and hybrid models to accelerate LES in reactive flows: Application to hydrogen/methane combustion
par: Zou, Xiangrui, et autres
Publié: (2025)

A unified view of elastic and elasto-inertial turbulence in channel flows at low and moderate Reynolds numbers
par: Rota, Giulio Foggi, et autres
Publié: (2023)

Improving aircraft performance using machine learning: a review
par: Clainche, Soledad Le, et autres
Publié: (2022)

Divergence-aware adaptive prediction framework for accelerating CFD simulations of unsteady flows
par: Zou, Xiangrui, et autres
Publié: (2026)

Modeling heart flow dynamics using numerical simulations to identify the vortex ring: a practical guide
par: Lazpita, Eneko, et autres
Publié: (2024)

An Adaptive Framework for Autoregressive Forecasting in CFD Using Hybrid Modal Decomposition and Deep Learning
par: Abadía-Heredia, Rodrigo, et autres
Publié: (2025)

Characterizing Intraventricular Flow Patterns via Modal Decomposition Techniques in Idealized Left Ventricle Models
par: Lazpita, Eneko, et autres
Publié: (2025)

Predictability of weakly turbulent systems from spatially sparse observations using data assimilation and machine learning
par: Gupta, Vikrant, et autres
Publié: (2024)

Efficient Reduced Order Modeling Based on HODMD to Predict Intraventricular Flow Dynamics
par: Lazpita, Eneko, et autres
Publié: (2025)

The coherent structures of EVP Fluid Flow Past a Circular Cylinder
par: Corrochano, Adrián, et autres
Publié: (2025)

Clustering the Flow: A Data-Driven Framework for Pattern Discovery in Fluid Dynamics
par: Martin, Juan Angel, et autres
Publié: (2026)

Coherent structures in Newtonian and viscoelastic turbulent planar jets
par: Amor, Christian, et autres
Publié: (2026)

Stochastic fluids with transport noise: Approximating diffusion from data using SVD and ensemble forecast back-propagation
par: Woodfield, James
Publié: (2024)

Prediction of turbulent energy based on low-rank resolvent modes and machine learning
par: Fan, Yitong, et autres
Publié: (2024)

Flow control by a hybrid use of machine learning and control theory
par: Ishize, Takeru, et autres
Publié: (2023)

Machine-learned flow estimation with sparse data -- exemplified for the rooftop of a UAV vertiport
par: Hou, Chang, et autres
Publié: (2024)

Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flames
par: Fush, S. Trevor, et autres
Publié: (2026)

Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy
par: Wang, Yunpeng, et autres
Publié: (2024)

Classifying acoustic cavitation with machine learning trained on multiple physical models
par: Gatica, Trinidad, et autres
Publié: (2024)

Statistical machine learning tools for probabilistic closures of turbulence models
par: Lemos, Julia Domingues, et autres
Publié: (2025)

Spectral adjoint-based assimilation of sparse data in unsteady simulations of turbulent flows
par: Plogmann, Justin, et autres
Publié: (2024)

Model fusion with physics-guided machine learning
par: Pawar, Suraj, et autres
Publié: (2021)

Wind farm layout optimization using a novel machine learning approach
par: Anjiraki, Mehrshad Gholami, et autres
Publié: (2025)

Experimental characterization of shock-separation interaction over wavy-shaped geometries through feature analysis
par: Braun, J., et autres
Publié: (2024)

Defiltering turbulent flow fields for Lagrangian particle tracking using machine learning techniques
par: Oura, Tomoya, et autres
Publié: (2024)

Mixed data-source transfer learning for a turbulence model augmented physics-informed neural network
par: Toma, Christian, et autres
Publié: (2026)

Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
par: Fukagata, Koji, et autres
Publié: (2025)

Machine-learning-based simulation of turbulent flows over periodic hills using a hybrid U-Net and Fourier neural operator framework
par: Wang, Yunpeng, et autres
Publié: (2025)

Predicting the dynamics of a gas pocket during breaking wave impacts using machine learning
par: Ezeta, Rodrigo, et autres
Publié: (2025)

Data-driven modeling for flow reconstruction from sparse temperature measurements
par: Wang, Xicheng, et autres
Publié: (2025)

Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)
par: Gurevich, Daniel R., et autres
Publié: (2021)

Triple decomposition and sparse representation for noisy pressure-sensitive paint data
par: Kubota, Koyo, et autres
Publié: (2024)

Enhancing generalizability of machine learning general effective-viscosity turbulence model via tensor basis normalization
par: Ji, Ziqi, et autres
Publié: (2024)