Model Order Reduction based on Runge-Kutta Neural Network

TL;DR

This paper explores a novel model order reduction technique combining Proper Orthogonal Decomposition with a Runge-Kutta Neural Network to improve nonlinear system modeling, tested on three simulation models.

Contribution

It introduces the use of RKNN for model reconstruction in MOR, comparing its performance with MLP and analyzing effects of input parameter variations.

Findings

RKNN effectively learns system derivatives for better state prediction.

Time-dependent input parameters improve model accuracy.

RKNN outperforms MLP in nonlinear system approximation.

Abstract

Model Order Reduction (MOR) methods enable the generation of real-time-capable digital twins, which can enable various novel value streams in industry. While traditional projection-based methods are robust and accurate for linear problems, incorporating Machine Learning to deal with nonlinearity becomes a new choice for reducing complex problems. Such methods usually consist of two steps. The first step is dimension reduction by projection-based method, and the second is the model reconstruction by Neural Network. In this work, we apply some modifications for both steps respectively and investigate how they are impacted by testing with three simulation models. In all cases Proper Orthogonal Decomposition (POD) is used for dimension reduction. For this step, the effects of generating the input snapshot database with constant input parameters is compared with time-dependent input parameters. For the model reconstruction step, two types of neural network architectures are compared: Multilayer Perceptron (MLP) and Runge-Kutta Neural Network (RKNN). The MLP learns the system state directly while RKNN learns the derivative of system state and predicts the new state as a Runge-Kutta integrator.