Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1055909

Xyce TM Parallel Electronic Simulator: Reference Guide, Version 5.3

Keiter, Eric Richard·Mei, Ting·Russo, Thomas V.·Rankin, Eric Lamont·Schiek, Richard Louis·Thornquist, Heidi K.·Verley, Jason C.·Fixel, Deborah A.·Coffey, Todd Stirling·Pawlowski, Roger Patrick·Santarelli, Keith R.·Warrender, Christina E.
View original source ↗Read source document ↗Export metadata ↓

Abstract

This document is a reference guide to the Xyce Parallel Electronic Simulator, and is a companion document to the Xyce Users’ Guide. The focus of this document is (to the extent possible) exhaustively list device parameters, solver options, parser options, and other usage details of Xyce. This document is not intended to be a tutorial. Users who are new to circuit simulation are better served by the Xyce Users’ Guide.

Explore related subjects

42 ENGINEERING96 KNOWLEDGE MANAGEMENT AND PRESERVATION97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Keiter, Eric Richard, Mei, Ting, Russo, Thomas V., Rankin, Eric Lamont, Schiek, Richard Louis, Thornquist, Heidi K., Verley, Jason C., Fixel, Deborah A., Coffey, Todd Stirling, Pawlowski, Roger Patrick, Santarelli, Keith R., Warrender, Christina E.. 2012-09-14. Xyce TM Parallel Electronic Simulator: Reference Guide, Version 5.3. https://doi.org/10.2172/1055909

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↗