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July 6, 2017
Title: Under-Approximation Computation Through Optimal Control
Abstract: Under-approximation provides a subset of the reachable set of an uncertain dynamical system which can then be used to formally falsify properties of quantitative models. Using Pontryagin’s principle, our approach computes an under-approximation for a linear combination of state variables of nonlinear ordinary differential equations and time-varying uncertainties. By a numerical comparison against state-of-the-art tools Flow^∗ and CORA, we show that our methodology provides tight under-approximations in benchmarks, and that it can scale to models that are out of reach with these over-approximation techniques.