Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1562422

Sensitivity Analysis in Xyce

Keiter, Eric R.·Swiler, Laura Painton·Russo, Thomas V.·Wilcox, Ian Zachary
View original source ↗Read source document ↗Export metadata ↓

Abstract

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 direct 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 Laboratories, under project LDRD 14-0788.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Keiter, Eric R., Swiler, Laura Painton, Russo, Thomas V., Wilcox, Ian Zachary. 2016-09-01. Sensitivity Analysis in Xyce. https://doi.org/10.2172/1562422

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗