text: This paper proposes to use neural network and gradient descent to automatically design for engineering tasks. It uses two networks, parameterization network and prediction network to model the mapping from design parameters to fitness. It uses back propagation (gradient descent) to improve the design. The method is evaluated on heat sink design and airfoil design. This paper targets at a potentially very useful application of neural networks that can have real world impacts. However, I have three main concerns: 1) Presentation. The organization of the paper could be improved. It mixes the method, the heat sink example and the airfoil example throughout the entire paper. Sometimes I am very confused about what is being described. My suggestion would be to completely separate these three parts: present a general method first, then use heat sink as the first experiment and airfoil as the second experiment. This organization would make the writing much clearer. 2) In the paragraph above Section 4.1, the paper made two arguments. I might be wrong, but I do not agree with either of them in general. First of all, "neural networks are good at generalizing to examples outside their train set". This depends entirely on whether the sample distribution of training and testing are similar and whether you have enough training examples that cover important sample space. This is especially critical if a deep neural network is used since overfitting is a real issue. Second, "it is easy to imagine a hybrid system where a network is trained on a simulation and fine tuned ...". Implementing such a hybrid system is nontrivial due to the reality gap. There is an entire research field about closing the reality gap and transfer learning. So I am not convinced by these two arguments made by this paper. They might be true for a narrow field of application. But in general, I think they are not quite correct. 3) The key of this paper is to approximate the dynamics using neural network (which is a continuous mapping) and take advantage of its gradient computation. However, many of dynamic systems are inherently discontinuous (collision/contact dynamics) or chaotic (turbulent flow). In those scenarios, the proposed method might not work well and we may have to resort to the gradient free methods. It seems that the proposed method works well for heat sink problem and the steady flow around airfoil, both of which do not fall into the more complex physics regime. It would be great that the paper could be more explicit about its limitations. In summary, I like the idea, the application and the result of this paper. The writing could be improved. But more importantly, I think that the proposed method has its limitation about what kind of physical systems it can model. These limitation should be discussed more explicitly and more thoroughly.