COMPOSING MODELING AND SIMULATION WITH MACHINE LEARNING IN JULIA

Abstract

In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build surrogates from component-based models, including Functional Mockup Units, using continuous-time echo state networks (CTESN). The foundation of this environment, ModelingToolkit.jl, is an acausal-modeling language which can compose the trained surrogates as components. We present the JuliaSim model library, consisting of differential-algebraic equations and pre-trained surrogates, which can be composed using the modeling system. We demonstrate a surrogate-accelerated approach on HVAC dynamics by showing that the CTESN surrogates capture dynamics at less than 4% error with an acceleration of 340x, and speed up design optimization by two orders of magnitude. We showcase the surrogate deployed in a co-simulation loop allowing engineers to explore the design space of a coupled system. Together this demonstrates a workflow for automating the integration of machine learning into traditional modeling and simulation.