No abstract

Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables. For photonic-device models, we find that this training becomes especially challenging as design regions grow larger than the optical wavelength. We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples. Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve, and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.

In this work, we develop T2API, a statistical machine translationbased tool that takes a given English description of a programming task as a query, and synthesizes the API usage template for the task by learning from training data. T2API works in two steps. First, it derives the API elements relevant to the task described in the input by statistically learning from a StackOverflow corpus of text descriptions and corresponding code. To infer those API elements, it also considers the context of the words in the textual input and the context of API elements that often go together in the corpus. The inferred API elements with their relevance scores are ensembled into an API usage by our novel API usage synthesis algorithm that learns the API usages from a large code corpus via a graph-based language model. Importantly, T2API is capable of generating new API usages from smaller, previously-seen usages. CCS Concepts •Software and its engineering → Software libraries and repositories; Integrated and visual development environments;

No abstract

No abstract