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Found 4 papers, 2 papers with code
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes.
Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations
However, a major obstacle in the development of data-driven turbulence models is the lack of training data.
Fluid Dynamics
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
This study demonstrates that the PIML approach is a computationally affordable technique for improving the accuracy of RANS-modeled Reynolds stresses for high-Mach-number turbulent flows when there is a lack of experiments and high-fidelity simulations.
BIG-bench Machine Learning
Physics-informed machine learning
Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
Convolutional neural networks are powerful tools for image segmentation and classification.
