Models and Methods: Catching Up to the 21st Century

摘要

Rapid advances in information technology over the past several decades have been a mixed blessing for modeling and simulation. The field has made huge advances in the ability to harness computational power and link critical resources through distributed architectures; however, the issue of designing models for reuse or integration at various levels of resolution remains. On a daily basis, we in the modeling and simulation arena are asked to produce a synthetic replica of any given part of our universe (from cell nucleation to space communication) at arbitrary and often multiple levels of resolution to assess vastly different problem. That alone is a daunting and unsolvable problem (reproduce the universe at any scale for any purpose – and do it now). To make life even more interesting, we are doing this using technology that is evolving rapidly, providing us with new capabilities in networking, middleware and architectures as well as in data management. The temptation is to evolve the methods and tools independently of the problem spaces – because we can imagine how to employ these new technologies. What is far more challenging is to retain all that technical knowledge and attempt to understand new problem domains; the ones we have neglected because they are outside our domain of experience and exhibit relationships that are “messier” than Newton’s laws. Imagining solutions to these problems is a bit harder. Thus the question: how do we capitalize on new technology while stepping back for a moment to explore the solution space for modeling advances in fields where the only knowledge we lack is the robust connective tissue – the relationships among critical parameters?