We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural network, perform gradient decent to maximize the fitness. We demonstrate this methods effectiveness by designing an optimized heat sink and both 2D and 3D airfoils that maximize the lift drag ratio under steady state flow conditions. We highlight that our method has two distinct benefits over other automated design approaches. First, evaluating the neural networks prediction of fitness can be orders of magnitude faster then simulating the system of interest. Second, using gradient decent allows the design space to be searched much more efficiently then other gradient free methods. These two strengths work together to overcome some of the current shortcomings of automated design.
A method for performing automated design on real world objects such as heat sinks and wing airfoils that makes use of neural networks and gradient descent. Neural network (parameterization and prediction) and gradient descent (back propogation) to automatically design for engineering tasks.  This paper introduces using a deep network to approximate the behavior of a complex physical system, and then design optimal devices by optimizing this network with respect to its inputs.