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

10/26/2021
by   Jonas Kneifl, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

03/04/2021

Time-dependent stochastic basis adaptation for uncertainty quantification

We extend stochastic basis adaptation and spatial domain decomposition m...
01/25/2022

Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks

We present a novel paradigm for modeling certain types of dynamic simula...
01/14/2021

Non-intrusive surrogate modeling for parametrized time-dependent PDEs using convolutional autoencoders

This work presents a non-intrusive surrogate modeling scheme based on ma...
06/26/2021

Analyzing and predicting non-equilibrium many-body dynamics via dynamic mode decomposition

Simulating the dynamics of a nonequilibrium quantum many-body system by ...
11/23/2020

Reduced Order Modeling for Parameterized Time-Dependent PDEs using Spatially and Memory Aware Deep Learning

We present a novel reduced order model (ROM) approach for parameterized ...
07/02/2021

RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing

A reinforcement-learning-based non-uniform compressed sensing (NCS) fram...
06/24/2019

Data-driven prediction of vortex-induced vibration response of marine risers subjected to three-dimensional current

Slender marine structures such as deep-water marine risers are subjected...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.