A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Data-driven aerospace engineering: reframing the industry with machine learning
Data science, and machine learning in particular, is rapidly transforming the scientific and
industrial landscapes. The aerospace industry is poised to capitalize on big data and …
industrial landscapes. The aerospace industry is poised to capitalize on big data and …
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics
Accurately modelling the nonlinear dynamics of a system from measurement data is a
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …
A review of uncertainty analysis in building energy assessment
Uncertainty analysis in building energy assessment has become an active research field
because a number of factors influencing energy use in buildings are inherently uncertain …
because a number of factors influencing energy use in buildings are inherently uncertain …
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
A paradigm for data-driven predictive modeling using field inversion and machine learning
EJ Parish, K Duraisamy - Journal of computational physics, 2016 - Elsevier
We propose a modeling paradigm, termed field inversion and machine learning (FIML), that
seeks to comprehensively harness data from sources such as high-fidelity simulations and …
seeks to comprehensively harness data from sources such as high-fidelity simulations and …
Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …
The use and misuse of mathematical modeling for infectious disease policymaking: lessons for the COVID-19 pandemic
LP James, JA Salomon, CO Buckee… - Medical Decision …, 2021 - journals.sagepub.com
Mathematical modeling has played a prominent and necessary role in the current
coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models …
coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models …
Modeling, analysis, and optimization under uncertainties: a review
Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …
has been an active area of research due to its evident advantages over deterministic design …
A tutorial on Bayesian inference to identify material parameters in solid mechanics
The aim of this contribution is to explain in a straightforward manner how Bayesian inference
can be used to identify material parameters of material models for solids. Bayesian …
can be used to identify material parameters of material models for solids. Bayesian …