Pages that link to "Item:Q5131422"
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The following pages link to Using machine learning to detect the turbulent region in flow past a circular cylinder (Q5131422):
Displaying 10 items.
- Multiresolution classification of turbulence features in image data through machine learning (Q2028142) (← links)
- Non-uniform dependence on initial data for the rotation-Camassa-Holm equation (Q2096356) (← links)
- A robust super-resolution reconstruction model of turbulent flow data based on deep learning (Q2139577) (← links)
- Machine learning for vortex induced vibration in turbulent flow (Q2670071) (← links)
- High-resolution fluid–particle interactions: a machine learning approach (Q5064921) (← links)
- Data-Driven, Physics-Based Feature Extraction from Fluid Flow Fields using Convolutional Neural Networks (Q5161377) (← links)
- Data-driven prediction of unsteady flow over a circular cylinder using deep learning (Q5235730) (← links)
- Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network (Q5884992) (← links)
- Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems (Q6588249) (← links)
- Turbulence scaling from deep learning diffusion generative models (Q6589908) (← links)
