Search NASA⌕ Search

DOE OSTI · 1562621

Electromagnetic Prediction and Propagation

Abstract

An electromagnetic finite volume forward solver is implemented to create a suite of forward models that provide the expected response for an air-filled buried structure constructed of concrete and rebar. Model parameters considered are the conductivities and thicknesses of a two-layer subsurface and the nature of VLF plane wave source. By building this suite of models, the results can be packaged into a data set that is both easily callable and requires minimal storage. More importantly, the user is relieved of the time required to manually execute a large number of models. Instead the results are already provided along with an interpolation tool for immediately data access. This document is written in compliance the LDRD reporting requirements for a close-out report on Project 180848.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Downs, Christine M., Weiss, Chester J., Bach, Jeffrey A.. 2016-09-01. Electromagnetic Prediction and Propagation. https://doi.org/10.2172/1562621

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

KEEP EXPLORING

Related reports

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↗