Image de couverture de livre

Convolutional Autoencoders, Clustering and POD for Low-dimensional Parametrization of Navier-Stokes Equations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kim, Yongho, Heiland, Jan
Format:	Preprint
Publié:	2023
Sujets:	Dynamical Systems Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows
par: Heiland, Jan, et autres
Publié: (2024)

Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design
par: Heiland, Jan, et autres
Publié: (2024)

Polytopic Autoencoders for Very Low‐Dimensional Parametrizations of Fluid Flow Models
par: Jan Heiland, et autres
Publié: (2025)

Autoencoding Dynamics: Topological Limitations and Capabilities
par: Kvalheim, Matthew D., et autres
Publié: (2025)

A Dynamics-Informed Gaussian Process Framework for 2D Stochastic Navier-Stokes via Quasi-Gaussianity
par: Hamzi, Boumediene, et autres
Publié: (2025)

A Comparison of Parametric Dynamic Mode Decomposition Algorithms for Thermal-Hydraulics Applications
par: Riva, Stefano, et autres
Publié: (2025)

Parametric 3D Convolutional Autoencoder for the Prediction of Flow Fields in a Bed Configuration of Hot Particles
par: Mjalled, Ali, et autres
Publié: (2023)

Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks
par: Kneifl, Jonas, et autres
Publié: (2024)

Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics
par: Samota, Sandeep Kumar, et autres
Publié: (2026)

Non-Negative Universal Differential Equations With Applications in Systems Biology
par: Philipps, Maren, et autres
Publié: (2024)

Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics
par: Samota, Sandeep Kumar, et autres
Publié: (2026)

Machine-Precision Prediction of Low-Dimensional Chaotic Systems
par: Schötz, Christof, et autres
Publié: (2025)

Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations
par: van Tegelen, Eva, et autres
Publié: (2025)

On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
par: Rajkumar, Santosh Mohan, et autres
Publié: (2026)

Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
par: Jeong, Minchan, et autres
Publié: (2025)

GN-SINDy: Greedy Sampling Neural Network in Sparse Identification of Nonlinear Partial Differential Equations
par: Forootani, Ali, et autres
Publié: (2024)

Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
par: Malpica-Morales, Antonio, et autres
Publié: (2024)

Automating the Discovery of Partial Differential Equations in Dynamical Systems
par: Li, Weizhen, et autres
Publié: (2024)

Gradient Flow Equations for Deep Linear Neural Networks: A Survey from a Network Perspective
par: Wendin, Joel, et autres
Publié: (2025)

Low-Cost Black-Box Detection of LLM Hallucinations via Dynamical System Prediction
par: Wilson, Dan, et autres
Publié: (2026)

A data free neural operator enabling fast inference of 2D and 3D Navier Stokes equations
par: Choi, Junho, et autres
Publié: (2025)

Parametric Taylor series based latent dynamics identification neural networks
par: Lin, Xinlei, et autres
Publié: (2024)

Enhancing Solutions for Complex PDEs: Introducing Complementary Convolution and Equivariant Attention in Fourier Neural Operators
par: Zhao, Xuanle, et autres
Publié: (2023)

Reservoir computing for system identification and predictive control with limited data
par: Williams, Jan P., et autres
Publié: (2024)

Quantitative Clustering in Mean-Field Transformer Models
par: Chen, Shi, et autres
Publié: (2025)

Reduced Order Modeling with Shallow Recurrent Decoder Networks
par: Tomasetto, Matteo, et autres
Publié: (2025)

Structure tensor Reynolds-averaged Navier-Stokes turbulence models with equivariant neural networks
par: Miller, Aaron, et autres
Publié: (2025)

Analysis of accuracy and efficiency of neural networks to simulate Navier-Stokes fluid flows with obstacles
par: Hespanha, Rui, et autres
Publié: (2025)

DUE: A Deep Learning Framework and Library for Modeling Unknown Equations
par: Chen, Junfeng, et autres
Publié: (2025)

Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations
par: van Tegelen, Eva, et autres
Publié: (2026)

Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging
par: Zakariaei, Niloufar, et autres
Publié: (2024)

An efficient hp-Variational PINNs framework for incompressible Navier-Stokes equations
par: Anandh, Thivin, et autres
Publié: (2024)

Neural Integral Equations
par: Zappala, Emanuele, et autres
Publié: (2022)

VAE-Var: Variational-Autoencoder-Enhanced Variational Assimilation
par: Xiao, Yi, et autres
Publié: (2024)

Duality of Navier-Stokes to a one-dimensional system
par: Migdal, Alexander
Publié: (2024)

Inertial manifolds for the two-dimensional hyperviscous Navier-Stokes equations
par: Guo, Yanqiu
Publié: (2024)

Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
par: Chen, Biao, et autres
Publié: (2026)

Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddings
par: Moore, Samuel A., et autres
Publié: (2024)

Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder
par: Gupta, Priyam, et autres
Publié: (2023)

Analysis of tidal flows through the Strait of Gibraltar using Dynamic Mode Decomposition
par: Dias, Sathsara, et autres
Publié: (2023)