A3 Book section, chapters in research books

Accelerating design processes using data-driven models


Open Access publication

Publication Details
Authors: Kurvinen Emil, Suninen Iines, Orzechowski Grzegorz, Choi Jin H., Kim Jin-Guym, Mikkola Aki
Editors of book: Ukko, Juhani; Saunila, Minna; Heikkinen, Janne; Semken, R. Scott; Mikkola, Aki
Edition name or number: 1
Publishing place: London
Publication year: 2021
Language: English
Title of parent publication: Real-time Simulation for Sustainable Production : Enhancing User Experience and Creating Business Value
ISBN: 978-0-367-51516-8
eISBN: 978-1-00-305421-8
JUFO level of this publication: 3
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2021060734334

Abstract

Data-driven modeling is a new paradigm in science and engineering for
dealing with design problems. In the past, collection of data was problematic
and scarce, and was mainly done to document experiments and verify research
hypotheses. Today, we are facing information explosion, as data became
affordable and simple to gather, store, and distribute. This became possible
due to availability and prevalence of sensors, high-volume storages, cloud
services, and internet of things solutions. Abundant data accessibility causes
serious challenges to the modern engineering, and we should learn how to take
advantageous of them in design processes.


Data-driven models have many important applications. Complete models can
be created based on gathered information without knowing the underlying
physics. Existing, working models can be improved, verified, and extend on
account of the data. In many important applications where strict simulation
times are crucial, like in control and human in the loop systems, data-driven modeling
can be an instant solution by providing highly efficient models. Moreover, the
new paradigm allows for emergence of novel engineering fields like digital
twins and virtual sensors. It can be stated that there are no modern design
processes without data utilization and, consequently, data-driven modeling.


Last updated on 2021-16-06 at 07:31