Work

A probabilistic graphical model foundation for enabling predictive digital twins at scale

Year: 2021

Type: article

Source: Nature Computational Science

DOI: 10.1038/s43588-021-00069-0

Authors: Michael G. Kapteyn, Jacob V. R. Pretorius, Karen E. Willcox

Institutions: Massachusetts Institute of Technology, The University of Texas at Austin

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: U.S. Department of Defense, U.S. Department of Energy, Multidisciplinary University Research Initiative, International Design Centre, Air Force Office of Scientific Research

Awards: G1523888516, G1841750192, G2077924196, G230078430, G3045592441, G3392305090, G3616359867, TAS::57 3600::TAS 'COUPLING IN UNCERTAIN MULTI-PHYSICS SYSTEMS', G5590208929, RISE OF THE MACHINES: ROBUST, INTERPRETABLE, SCALABLE, EFFICIENT DECISION SUPPORT, AEOLUS: ADVANCES IN EXPERIMENTAL DESIGN, OPTIMAL CONTROL, AND LEARNING FOR UNCERTAIN COMPLEX SYSTEMS, TAS::57 3600::TAS "(MURI 14)-A UNIFIED MATHEMATICAL AND ALGORITHMIC FRAMEWORK FOR MANAGING MULTIPLE INFORMATION SOURCES OF MULTI-PHYSICS SYSTEMS", G6587744491, G6971160362, TAS::57 3600::TAS "DYNAMIC DATA DRIVEN METHODS FOR SELF-AWARE AEROSPACE VEHICLES", DATED 19 JUN 2014 (THE GRANTEE'S TECHNICAL PROPOSAL) IS HEREBY

OpenAlex ID: W3112364397

FWCI: 20.61

Cites: 32

Cited by: 437

Related to: 10