Machine learning surrogates of high-fidelity electrical models
Abstract not provided.
Publications
Extraction of Data-Driven Compact Models from High-Fidelity Semiconductor Simulations with Topological Data Analysis
Abstract not provided.
Gradient-Enhanced Polynomial Chaos Methods for Circuit Simulation
Abstract not provided.
Gradient-Enhanced Polynomial Chaos Methods for Circuit Simulation
Abstract not provided.
Gradient-Enhanced Polynomial Chaos Methods for Circuit Simulation
Abstract not provided.
Gradient-Enhanced Polynomial Chaos Methods for Circuit Simulation
Abstract not provided.
Sensitivity Analysis in Xyce
Parametric sensitivities of dynamic system responses are very useful in a variety of applications, including circuit optimization and uncertainty quantification. Sensitivity calculation methods fall into two related categories: direct and adjoint methods. Effective implementation of such methods in a production circuit simulator poses a number of technical challenges, including instrumentation of device models. This report documents several years of work developing and implementing di- rect and adjoint sensitivity methods in the Xyce circuit simulator. Much of this work sponsored by the Laboratory Directed Research and Development (LDRD) Program at Sandia National Labora- tories, under project LDRD 14-0788.
Advanced Uncertainty Quantification Methods for Circuit Simulation: Final Report LDRD 2016-0845
This report summarizes the methods and algorithms that were developed on the Sandia National Laboratory LDRD project entitled "Advanced Uncertainty Quantification Methods for Circuit Sim- ulation", which was project # 173331 and proposal # 2016-0845. As much of our work has been published in other reports and publications, this report gives an brief summary. Those who are in- terested in the technical details are encouraged to read the full published results and also contact the report authors for the status of follow-on projects.
8 Results