A deep learning modeling framework to capture mixing patterns in reactive-transport systems
This work addresses a problem in fields like subsurface transport and climate modeling by providing a more efficient modeling approach, though it appears incremental as it adapts existing deep-learning techniques to a specific domain.
The paper tackles the challenge of predicting chemical mixing in reactive-transport systems, which is computationally intensive and data-inefficient with existing methods, by introducing a deep-learning framework that combines CNNs and LSTMs to ensure non-negativity and achieves fast, accurate predictions with minimal training data.
Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep-learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework -- placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training.