Pages that link to "Item:Q2672202"

The following pages link to Surrogate convolutional neural network models for steady computational fluid dynamics simulations (Q2672202):

Displaying 10 items.

• A CNN-based shock detection method in flow visualization (Q1739782) (← links)
• Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• Output-based adaptive aerodynamic simulations using convolutional neural networks (Q2245362) (← links)
• Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
• Training a Neural-Network-Based Surrogate Model for Aerodynamic Optimisation Using a Gaussian Process (Q5880409) (← links)
• Fluid Flow Modelling Using Physics-Informed Convolutional Neural Network in Parametrised Domains (Q6092912) (← links)
• \(\mathrm{U}^p\)-net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics (Q6159312) (← links)
• DNN-MG: a hybrid neural network/finite element method with applications to 3D simulations of the Navier-Stokes equations (Q6194150) (← links)
• A short note on solving partial differential equations using convolutional neural networks (Q6620254) (← links)