Pages that link to "Item:Q5243565"

The following pages link to Nonlinear mode decomposition with convolutional neural networks for fluid dynamics (Q5243565):

Displaying 29 items.

• A CNN-based shock detection method in flow visualization (Q1739782) (← links)
• Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression (Q2083714) (← links)
• Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models (Q2111169) (← links)
• Physics-informed machine learning for reduced-order modeling of nonlinear problems (Q2133556) (← links)
• Unsteady flow prediction from sparse measurements by compressed sensing reduced order modeling (Q2138820) (← links)
• Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
• Dimensionality reduction through convolutional autoencoders for fracture patterns prediction (Q2691969) (← links)
• A data-driven model based on modal decomposition: application to the turbulent channel flow over an anisotropic porous wall (Q3390383) (← links)
• A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations (Q5005016) (← links)
• Application of physics-constrained data-driven reduced-order models to shape optimization (Q5022975) (← links)
• Image-based flow decomposition using empirical wavelet transform (Q5144528) (← links)
• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows (Q5145046) (← links)
• Sparse identification of nonlinear dynamics with low-dimensionalized flow representations (Q5154682) (← links)
• Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains (Q5162374) (← links)
• Residual dynamic mode decomposition: robust and verified Koopmanism (Q5871684) (← links)
• Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network (Q5884992) (← links)
• Space-time adaptive model order reduction utilizing local low-dimensionality of flow field (Q6048451) (← links)
• Nonlinear reduced-order modeling for three-dimensional turbulent flow by large-scale machine learning (Q6060754) (← links)
• Predicting turbulent dynamics with the convolutional autoencoder echo state network (Q6067855) (← links)
• Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow (Q6080313) (← links)
• Multiscale graph neural network autoencoders for interpretable scientific machine learning (Q6087967) (← links)
• Airfoil-based convolutional autoencoder and long short-term memory neural network for predicting coherent structures evolution around an airfoil (Q6100106) (← links)
• Learning proper orthogonal decomposition of complex dynamics using heavy-ball neural ODEs (Q6101554) (← links)
• The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems (Q6108137) (← links)
• \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications (Q6116133) (← links)
• An incremental singular value decomposition approach for large-scale spatially parallel \& distributed but temporally serial data -- applied to technical flows (Q6151883) (← links)
• Multi-scale time-stepping of partial differential equations with transformers (Q6550140) (← links)
• A surrogate reduced order model of the unsteady advection dominant problems based on combination of deep autoencoders-LSTM and POD (Q6645093) (← links)
• Application of deep learning reduced-order modeling for single-phase flow in faulted porous media (Q6662482) (← links)