(549e) Learning Coarse-Scale ODEs/PDEs from Microscopic Data: What and How Can We Learn It from Data?
- Conference: AIChE Annual Meeting
- Year: 2021
- Proceeding: 2021 Annual Meeting
- Group: Bridging the Skills Gap in Chemical Engineering
- Time: Monday, November 15, 2021 - 9:20am-9:40am
Nowadays, thanks to the advances in machine learning techniques and data-driven modeling, we are able to effectively identify ODEs/PDEs from data directly without prior knowledge. To this end, the prerequisite for learning data-driven ODEs/PDEs will gradually evolve from the classical, mechanistic/physics based prerequisite courses in chemical engineering to include advanced statistics, machine learning techniques, and data mining. In this talk, we present some machine learning techniques of data-driven ODE/PDEs from microscopic data us (e.g. the use of ordinary neural network , Gaussian process , or ResNet ) Through these examples, we illustrate the concept of the black-box model and the (partially physics informed) gray box model to identify/explain model ODE/PDE. Moreover, we present a new challenge in data-driven ODE/PDE: (1) how to choose the right variables from data, (2) how to construct a proper data-driven model, and (3) what are the pros and cons for different approaches. Finally, this presentation will suggest a new direction for a future curriculum for data-driven modeling in chemical engineering.
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