Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems
  via Abstraction   In this paper, we consider the data-driven model invalidation problem for
Lipschitz continuous systems, where instead of given mathematical models, only
prior noisy sampled data of the systems are available. We show that this
data-driven model invalidation problem can be solved using a tractable
feasibility check. Our proposed approach consists of two main components: (i) a
data-driven abstraction part that uses the noisy sampled data to
over-approximate the unknown Lipschitz continuous dynamics with upper and lower
functions, and (ii) an optimization-based model invalidation component that
determines the incompatibility of the data-driven abstraction with a newly
observed length-T output trajectory. Finally, we discuss several methods to
reduce the computational complexity of the algorithm and demonstrate their
effectiveness with a simulation example of swarm intent identification.

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The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol