Generating Design Suggestions under Tight Constraints with Gradient‐based Probabilistic Programming

We present a system for generating suggestions from highly‐constrained, continuous design spaces. We formulate suggestion as sampling from a probability distribution; constraints are represented as factors that concentrate probability mass around sub‐manifolds of the design space. These sampling problems are intractable using typical random walk MCMC techniques, so we adopt Hamiltonian Monte Carlo (HMC), a gradient‐based MCMC method. We implement HMC in a high‐performance probabilistic programming language, and we evaluate its ability to efficiently generate suggestions for two different, highly‐constrained example applications: vector art coloring and designing stable stacking structures.