An efficient method for the numerical integration of measured variable dependent ordinary differential equations
The proposed numerical integration method can efficiently integrate ODEs which incorporate frequently measured variables. The method only evaluates ODEs at time instance at which the measurements are available, such avoiding interpolation. For the integration of dynamic neural networks or hybrid models the method can significantly reduce the number of function evaluations as compared to fixed step-size methods.
numerical integration ordinary differential equations differential equations dynamic neural networks hybrid semi-parametric modeling