6 research outputs found
Lagrangian Reachabililty
We introduce LRT, a new Lagrangian-based ReachTube computation algorithm that
conservatively approximates the set of reachable states of a nonlinear
dynamical system. LRT makes use of the Cauchy-Green stretching factor (SF),
which is derived from an over-approximation of the gradient of the solution
flows. The SF measures the discrepancy between two states propagated by the
system solution from two initial states lying in a well-defined region, thereby
allowing LRT to compute a reachtube with a ball-overestimate in a metric where
the computed enclosure is as tight as possible. To evaluate its performance, we
implemented a prototype of LRT in C++/Matlab, and ran it on a set of
well-established benchmarks. Our results show that LRT compares very favorably
with respect to the CAPD and Flow* tools.Comment: Accepted to CAV 201
Lagrangian Reachtubes: The Next Generation
We introduce LRT-NG, a set of techniques and an associated toolset that
computes a reachtube (an over-approximation of the set of reachable states over
a given time horizon) of a nonlinear dynamical system. LRT-NG significantly
advances the state-of-the-art Langrangian Reachability and its associated tool
LRT. From a theoretical perspective, LRT-NG is superior to LRT in three ways.
First, it uses for the first time an analytically computed metric for the
propagated ball which is proven to minimize the ball's volume. We emphasize
that the metric computation is the centerpiece of all bloating-based
techniques. Secondly, it computes the next reachset as the intersection of two
balls: one based on the Cartesian metric and the other on the new metric. While
the two metrics were previously considered opposing approaches, their joint use
considerably tightens the reachtubes. Thirdly, it avoids the "wrapping effect"
associated with the validated integration of the center of the reachset, by
optimally absorbing the interval approximation in the radius of the next ball.
From a tool-development perspective, LRT-NG is superior to LRT in two ways.
First, it is a standalone tool that no longer relies on CAPD. This required the
implementation of the Lohner method and a Runge-Kutta time-propagation method.
Secondly, it has an improved interface, allowing the input model and initial
conditions to be provided as external input files. Our experiments on a
comprehensive set of benchmarks, including two Neural ODEs, demonstrates
LRT-NG's superior performance compared to LRT, CAPD, and Flow*.Comment: 12 pages, 14 figure
CAPD::DynSys: a flexible C++ toolbox for rigorous numerical analysis of dynamical systems
We present the CAPD::DynSys library for rigorous numerical analysis of
dynamical systems. The basic interface is described together with several
interesting case studies illustrating how it can be used for computer-assisted
proofs in dynamics of ODEs.Comment: 25 pages, 4 figures, 11 full C++ example