  In this paper we introduce JuliaSim, a high-performance programming
environment designed to blend traditional modeling and simulation with machine
learning. JuliaSim can build accelerated surrogates from component-based
models, such as those conforming to the FMI standard, 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 within its staged compilation process. As a
complementary factor we present the JuliaSim model library, a standard library
with differential-algebraic equations and pre-trained surrogates, which can be
composed using the modeling system for design, optimization, and control. We
demonstrate the effectiveness of the surrogate-accelerated modeling and
simulation approach on HVAC dynamics by showing that the CTESN surrogates
accurately capture the dynamics of a HVAC cycle at less than 4\% error while
accelerating its simulation by 340x. We illustrate the use of surrogate
acceleration in the design process via global optimization of simulation
parameters using the embedded surrogate, yielding a speedup of two orders of
magnitude to find the optimum. We showcase the surrogate deployed in a
co-simulation loop, as a drop-in replacement for one of the coupled FMUs,
allowing engineers to effectively explore the design space of a coupled system.
Together this demonstrates a workflow for automating the integration of machine
learning techniques into traditional modeling and simulation processes.
