The Advanced Simulation and Computing (ASC) initiative to maximize near and long-term Artificial Intelligence (AI) and Machine Learning (ML) technologies on Sandia’s Nuclear Deterrence (ND) program funded a project focused on producing physics-aware machine learned compact device models suited for use in production circuit simulators such as Xyce. While the original goal was only to make a demonstration of these capabilities, the team worked closely with Xyce developers to ensure the resulting product would be suitable for the already large group of Xyce users both internal and external to Sandia. This was done by extending the existing C++ general external interface in Xyce and adding Pybind11 hooks. The result is that with release 7.3 of Xyce, the ability to define machine learned compact device models entirely in Python (the most commonly used machine learning language) and use them with Xyce will be publicly available.
