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Found 2 papers, 1 papers with code
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models.
Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing
An attractive aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes.
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