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Found 3 papers, 1 papers with code
Machine Learning Techniques for Data Reduction of CFD Applications
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications.
Transfer learning for predicting source terms of principal component transport in chemically reactive flow
The performance of the reduced-order model with a sparse dataset is found to be remarkably enhanced if the training of the ANN model is restricted by a regularization term that controls the degree of knowledge transfer from source to target tasks.
The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning
To illustrate this point, we demonstrate that deep learning models, trained and tested on data from a petascale CFD simulation, are robust to errors introduced during lossy compression in a semantic segmentation problem.
