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Found 5 papers, 1 papers with code
Physics-Constrained Generative Adversarial Networks for 3D Turbulence
Generative Adversarial Networks (GANs) have received wide acclaim among the machine learning (ML) community for their ability to generate realistic 2D images.
Wavelet-Powered Neural Networks for Turbulence
One of the fundamental driving phenomena for applications in engineering, earth sciences and climate is fluid turbulence.
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence
Deep learning approaches have shown much promise for physical sciences, especially in dimensionality reduction and compression of large datasets.
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence
In the recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences.
Computational Physics
A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks
Reduced Order Modeling (ROM) for engineering applications has been a major research focus in the past few decades due to the unprecedented physical insight into turbulence offered by high-fidelity CFD.
Computational Physics Fluid Dynamics
