Our paper “Physics-Informed Recurrent Neural Networks and Hyper-parameter Optimization for Dynamic Process Systems” has been accepted for publication in Computers and Chemical Engineering! Great job Tuse Asrav!

https://www.sciencedirect.com/science/article/pii/S0098135423000649

In this study, two different physics-informed training approaches are investigated. The first approach is using a multi-objective loss function in the training including the discretized form of the differential equation. The second approach is using a hybrid recurrent neural network cell with embedded physics-informed and data-driven nodes performing Euler discretization.

Physics-informed neural networks can improve test performance even though decrease in training performance might be observed. Finally, smaller and more robust architectures are obtained from hyper-parameter optimization when physics-informed training is performed.