Multiscale representation of simulated time

Abstract

To better support multiscale modeling and simulation, we present a multiscale time representation consisting of data types, data structures, and algorithms that collectively support the recording of past events and scheduling of future events in a discrete event simulation. Our approach addresses the drawbacks of conventional time representations: limited range in the case of 32-or 64-bit fixed-point time values; problematic rounding errors in the case of floating-point numbers; and the lack of a universally acceptable precision level in the case of brute force approaches. The proposed representation provides both extensive range and fine resolution in the timing of events, yet it stores and manipulates the majority of event times as standard 64-bit numbers. When adopted for simulation purposes, the representation allows a domain expert to choose a precision level for his/her model. This time precision is honored by the simulator even when the model is integrated with other models of vastly different time scales. Making use of C++11 programming language features and the Discrete Event System Specification formalism, we implemented a simulator to test the time representation and inform a discussion on its implications for collaborative multiscale modeling efforts.

Advanced Cell-DEVS modeling applications: a legacy of Norbert Giambiasi

“…CD ++ is a tool built to implement DEVS and Cell-DEVS models. 7 There are numerous tools, and some recent efforts include DesignDEVS, 75 DEVS-SOA, 76 PythonDEVS, 77 and others. [78][79][80] The tool allows models to be defined according to the specifications introduced in the previous section.…”

“…The formal specifications for Cell-DEVS models were defined by different teams of students throughout the years, based on the formal specifications built based on our discussions with Norbert. The tools are different instantiations of the CD ++ toolkit, 7 which was originally built to simulate DEVS and Cell-DEVS models. CD ++ was originally built including a high level modeling language for binary CA combined with timing delays.…”

“…The original ideas were presented elsewhere. [3][4][5][6] It is important to notice that each of the cells in the cell space is updated at different times (a real number, or extended representations like the ones defined in Goldstein et al), 7 as set by a rule's delay component. This is a clear departure from the classical approach to CA, where all active cells are updated at the same time, thus reducing computation time.…”

“…The Cell-DEVS formalism has been used for modeling and simulation of varied phenomena, 8–11 based on our work with Norbert and our original discussions. This non-comprehensive list can be classified in various areas: traffic, 12–16 physics, 17–19 environmental modeling, 19–25 networking, 26–29 architecture and construction, 30–34 biology and medicine, 35–39 defense and emergency planning, 40–48 social models, 48–50 crowd modeling, 51…”

Sequential decision making and simulation-optimization for the design of complex engineering systems

“…Evaluating engineering system design often requires the use of a large-scale and computationally expensive simulation model. For example, designing power plants (Amin, 2011), architectural design (Goldstein and Khan, 2017), and mechanical systems (Goldstein et al, 2018;Berquist et al, 2017) may require the use of complex simulations to evaluate the performance of the system and the effect of multiple sources of uncertainty in the design (Shabani and Sowlati, 2016a). Computing the optimal policy for a system whose objective function is evaluated via a simulation in sequential settings is challenging .…”