Abstract: A system and method including receiving a set of deep neural networks (DNN) including DNNs trained with an embedded trojan and DNNs trained without any embedded trojan, each of the trained DNNs being represented by a mathematical formulation learned by the DNNs and expressing a relationship between an input of the DNNs and an output of the DNNs; extracting at least one characteristic feature from the mathematical formulation of each of the trained DNNs; statistically analyzing the at least one characteristic feature to determine whether there is a difference between the DNNs trained with the embedded trojan and the DNNs trained without any embedded trojan; generating, in response to the determination indicating there is a difference, a detector model to execute the statistical analyzing on deep neural networks; and storing a file including the generated detector model in a memory device.

Abstract: The example embodiments are directed to a system and method for predicting a flow about an object through the use of a predictive model instead of a machine simulation. Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD flow in seconds which greatly improves design time. In one example, the method may include receiving input data comprising shape parameters of a geometric object and flow parameters associated with the geometric object, predicting, via execution of a predictive model, a computational fluid dynamic (CFD) flow about the geometric object based on the shape parameters and the flow parameters included in the input data, and outputting one or more attributes of the predicted CFD flow about the geometric object via a display device.

Abstract: The example embodiments are directed to a system and method for predicting a flow about an object through the use of a predictive model instead of a machine simulation. Traditional CFD simulations can take hours, even days. The example embodiments provide a predictive model that can predict a CFD flow in seconds which greatly improves design time. In one example, the method may include receiving input data comprising shape parameters of a geometric object and flow parameters associated with the geometric object, predicting, via execution of a predictive model, a computational fluid dynamic (CFD) flow about the geometric object based on the shape parameters and the flow parameters included in the input data, and outputting one or more attributes of the predicted CFD flow about the geometric object via a display device.