Multiscale modeling: foundations, historical milestones, current status,
and future prospects
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.