Abstract
Deep Neural Networks (DNNs) have gained tremendous popularity in many application areas over the recent years. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for system safety.In this paper, we present a framework to support testing of DNNs. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNNs used in Aerospace systems and also supports a comparison between different DNNs. In this paper, we will describe our statistical framework, the algorithms for model construction and the metric guiding the test case generation process.We will present a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a Boeing 747-100 aircraft.
Published Version
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