Generation of Sparse Jacobians for the Function Mock-Up Interface 2.0

In English Derivatives, or Jacobians, are commonly required by numerical algorithms. Access to accurate Jacobians often improves the performance and robustness of algorithms, and in addition, efficient implementation of Jacobian computations can reduce the over-all execution time. In this paper, we present methods for computing Jacobians in the context of the Functional Mock-up Interface (FMI), and Modelica. Two prototype implementations, in JModelica.org and OpenModelica are presented and compared in industrial benchmarks.