Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2480113

Xyce™ Parallel Electronic Simulator (V.7.9) Release Notes

Thornquist, Heidi Krista·Keiter, Eric R.·Schiek, Richard·Mei, Ting·Verley, Jason Christopher·Aadithya, Karthik Venkatraman·Schickling, Joshua David·Hennigan, Gary Lee
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Xyce™ Parallel Electronic Simulator has been written to support the simulation needs of Sandia National Laboratories’ electrical designers. Xyce™ is a SPICE-compatible simulator with the ability to solve extremely large circuit problems on large-scale parallel computing platforms, but also includes support for most popular parallel and serial computers. For up-to-date information not available at the time these notes were produced, please visit the Xyce™ web page at http://xyce.sandia.gov.

Explore related subjects

42 ENGINEERING97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Thornquist, Heidi Krista, Keiter, Eric R., Schiek, Richard, Mei, Ting, Verley, Jason Christopher, Aadithya, Karthik Venkatraman, Schickling, Joshua David, Hennigan, Gary Lee. 2024-09-12. Xyce™ Parallel Electronic Simulator (V.7.9) Release Notes. https://doi.org/10.2172/2480113

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↗