2020/08/16 by Ravi Radhakrishnan · 7 citations
Computer Science · Engineering · Physics and Astronomy · #Advanced Mathematical Modeling in Engineering #Complexity science #Computer science #Data science #Engineering #Engineering ethics #Epistemology #Face (sociological concept) #Grand Challenges #Implementation #Lattice Boltzmann Simulation Studies #Management science #Paradigm shift #Science and engineering #Social science #Sociology #Software engineering #Supercomputer #Theoretical and Computational Physics
paper · pdf · doi:10.1002/aic.17026
openalex publication_date 2020/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.