Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution. Issue 2 (28th May 2021)

• Record Type: Journal Article
• Authors: San, Omer; Rasheed, Adil; Kvamsdal, Trond
• Other Names: Benner Peter guestEditor.; Klawonn Axel guestEditor.; Stoll Martin guestEditor.
• Is Part Of: Mitteilungen der Gesellschaft für Angewandte Mathematik und Mechanik. Volume 44:Issue 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-28
• Subjects: digital twin -- hybrid analysis and modeling -- interface learning -- neurophysical computing -- reduced order modeling -- scientific machine learning; Mathematics -- Periodicals; Mechanics, Applied -- Periodicals; 510.5
• DOI: 10.1002/gamm.202100007
• Languages: English
• ISSNs: 0936-7195

Abstract

Abstract: Most modeling approaches lie in either of the two categories: physics‐based or data‐driven. Recently, a third approach which is a combination of these deterministic and statistical models is emerging for scientific applications. To leverage these developments, our aim in this perspective paper is centered around exploring numerous principle concepts to address the challenges of (i) trustworthiness and generalizability in developing data‐driven models to shed light on understanding the fundamental trade‐offs in their accuracy and efficiency and (ii) seamless integration of interface learning and multifidelity coupling approaches that transfer and represent information between different entities, particularly when different scales are governed by different physics, each operating on a different level of abstraction. Addressing these challenges could enable the revolution of digital twin technologies for scientific and engineering applications.