A Quantization Approach for Solving IVPs with DEVS

We show how Initial Value Problems can be solved using a quantization algorithm, and analyse various properties of this approach. Quantization is the dual of discretization, in that the dependent variables' space is partitioned (rather than the independent variable's). The quantization approach is appealing as it better matches the discrete-event simulation scheme than the discretization approach. First, a non-adaptive quantization algorithm, expressed in the DEVS formalism and based on the Forward-Euler approximation, is presented. We show that consistency as well as convergence are respected for autonomous systems, but cannot be guaranteed for nonautonomous problems. Absolute-stability as it is usually dened is gen-erally not achieved. We then introduce an adaptive quantization algorithm, which improves the overall performance of its non-adaptive counterpart.