Pages that link to "Item:Q3296524"

The following pages link to Machine Learning for Fluid Mechanics (Q3296524):

Displaying 50 items.

• Assimilation of disparate data for enhanced reconstruction of turbulent mean flows (Q2245373) (← links)
• A review on deep reinforcement learning for fluid mechanics (Q2245392) (← links)
• Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network (Q2334461) (← links)
• Machine learning in sedimentation modelling. (Q2490829) (← links)
• Machine learning for vortex induced vibration in turbulent flow (Q2670071) (← links)
• Coupled and uncoupled dynamic mode decomposition in multi-compartmental systems with applications to epidemiological and additive manufacturing problems (Q2670383) (← links)
• Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
• ModalPINN: an extension of physics-informed neural networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors (Q2672754) (← links)
• Output-weighted and relative entropy loss functions for deep learning precursors of extreme events (Q2677801) (← links)
• Data driven modeling of interfacial traction-separation relations using a thermodynamically consistent neural network (Q2678537) (← links)
• SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
• A projection-based, semi-implicit time-stepping approach for the Cahn-Hilliard Navier-Stokes equations on adaptive octree meshes (Q2683096) (← links)
• Detection of multiple interacting features of different strength in compressible flow fields (Q2683249) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics (Q2693664) (← links)
• PDE-constrained models with neural network terms: optimization and global convergence (Q2699336) (← links)
• Reinforcement-learning-based control of confined cylinder wakes with stability analyses (Q3383631) (← links)
• Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures (Q3388856) (← links)
• Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (Q3389009) (← links)
• Large-eddy simulation on the similarity between wakes of wind turbines with different yaw angles (Q4957321) (← links)
• Spatio-temporal microstructure of sprays: data science-based analysis and modelling (Q4964063) (← links)
• The turbulent dynamo (Q4964123) (← links)
• Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network (Q4986840) (← links)
• Data-driven resolvent analysis (Q4989070) (← links)
• 9 From the POD-Galerkin method to sparse manifold models (Q4993250) (← links)
• A Deep Learning Approach for the Computation of Curvature in the Level-Set Method (Q4997376) (← links)
• Coherent structure identification in turbulent channel flow using latent Dirichlet allocation (Q5000021) (← links)
• Cluster-based hierarchical network model of the fluidic pinball – cartographing transient and post-transient, multi-frequency, multi-attractor behaviour (Q5022961) (← links)
• Application of physics-constrained data-driven reduced-order models to shape optimization (Q5022975) (← links)
• Physics-guided deep learning for generating turbulent inflow conditions (Q5029513) (← links)
• High-resolution fluid–particle interactions: a machine learning approach (Q5064921) (← links)
• VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity (Q5065192) (← links)
• Machine learning active-nematic hydrodynamics (Q5073282) (← links)
• Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
• Interpreted machine learning in fluid dynamics: explaining relaminarisation events in wall-bounded shear flows (Q5077267) (← links)
• Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)
• Leveraging reduced-order models for state estimation using deep learning (Q5113091) (← links)
• Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples (Q5114256) (← links)
• Using machine learning to detect the turbulent region in flow past a circular cylinder (Q5131422) (← links)
• The<i>l</i>₁-based sparsification of energy interactions in unsteady lid-driven cavity flow (Q5131427) (← links)
• The structure of reconstructed flows in latent spaces (Q5139746) (← links)
• Interpreting neural network models of residual scalar flux (Q5144568) (← links)
• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows (Q5145046) (← links)
• Unsupervised deep learning for super-resolution reconstruction of turbulence (Q5145486) (← links)
• The most robust representations of flow trajectories are Lagrangian coherent structures (Q5157299) (← links)
• Phase-based control of periodic flows (Q5157303) (← links)
• Unsupervised modelling of a transitional boundary layer (Q5158482) (← links)
• Deep neural networks for waves assisted by the Wiener–Hopf method (Q5160881) (← links)
• Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks (Q5161377) (← links)
• Data-driven discovery of governing equations for fluid dynamics based on molecular simulation (Q5222604) (← links)