Robust Aerodynamic Shape Optimization using Deep Reinforcement Learning and NIGnets

Advisor: Prof. Juan J. Alonso
Affiliation: Aerospace Design Lab, Stanford University

We design airfoils that achieve high Lift-to-Drag ratios.

We formulate aerodynamic shape optimization as a Deep RL problem. To tackle the problems caused by self-intersection produced during optimization we use NIGnets, a new neural architecture that we developed that gives a hard guarantee on non-self-intersecting geometry representation. A NIGnet as a whole with a particular setting of its weights and biases (denoted by $\phi$) represents a particular shape. Our shape optimization approach can be summarized as follows:

1. Represent state (shape) using NIGnet parameters $\phi$.
2. An action corresponds to outputting $\Delta \phi$ that perturbs the NIGnet parameters to $\phi' = \phi + \Delta \phi$.
3. A reward is produced at each step that corresponds to the $\frac{L}{D}$ ratio of the current shape.

Since the NIGnet for any set of parameters $\phi$ always represents non-self-intersecting geometry, it will constrain the design space to only the set of physically reasonable shapes and therefore allow an RL agent to perform ambitious shape optimization. We also utilize Hindsight Experience Replay (HER) to train more general policies that can design for any target lift-to-drag ratio.