Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme

• URL: http://arxiv.org/abs/2110.13583v1
• Date: Tue, 26 Oct 2021 11:33:11 GMT
• Title: Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme
• Authors: Jonas Kneifl, Julian Hay, J\"org Fehr
• Abstract summary: This paper introduces a novel two-step MOR scheme to tackle this issue. It is concluded that the presented method is well suited to approximate parameterized ODEs and can handle time-dependent parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent research in non-intrusive data-driven model order reduction (MOR) enabled accurate and efficient approximation of parameterized ordinary differential equations (ODEs). However, previous studies have focused on constant parameters, whereas time-dependent parameters have been neglected. The purpose of this paper is to introduce a novel two-step MOR scheme to tackle this issue. In a first step, classic MOR approaches are applied to calculate a low-dimensional representation of high-dimensional ODE solutions, i.e. to extract the most important features of simulation data. Based on this representation, a long short-term memory (LSTM) is trained to predict the reduced dynamics iteratively in a second step. This enables the parameters to be taken into account during the respective time step. The potential of this approach is demonstrated on an occupant model within a car driving scenario. The reduced model's response to time-varying accelerations matches the reference data with high accuracy for a limited amount of time. Furthermore, real-time capability is achieved. Accordingly, it is concluded that the presented method is well suited to approximate parameterized ODEs and can handle time-dependent parameters in contrast to common methods.

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