Jan 11, 2021 · We will present in this paper a deep-learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions.

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 ...

A deep learning modeling framework to capture mixing patterns in reactive-transport systems. An e-print of the paper is available on arXiv. Authored by. N. V. ...

Sep 10, 2024 · This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for ...

Abstract. Prediction and control of chemical mixing are vital for many scientific ar- eas such as subsurface reactive transport, climate modeling, ...

This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal ...

Publication - Journal Article A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems ...

13 hours ago · ... (A Deep Learning Modeling Framework To Capture Mixing Patterns In Reactive-transport Systems). ... mixing are vital for many scientific areas ...

A deep learning modeling framework to capture mixing patterns in reactive-transport systems ... Learning Models for Predicting the State of Reactive Mixing.

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest ( ...