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Multiscale modeling: foundations, historical milestones, current status, and future prospects
  • Ravi Radhakrishnan
Ravi Radhakrishnan
University of Pennsylvania
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Abstract

Research problems in the domains of physical, engineering, biological sciences, often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

Peer review status:ACCEPTED

05 Jun 2020Submitted to AIChE Journal
09 Jun 2020Submission Checks Completed
09 Jun 2020Assigned to Editor
11 Jun 2020Reviewer(s) Assigned
05 Aug 2020Editorial Decision: Revise Minor
09 Aug 20201st Revision Received
10 Aug 2020Submission Checks Completed
10 Aug 2020Assigned to Editor
13 Aug 2020Editorial Decision: Accept