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DOE OSTI · 3011342

Discovering Physically Meaningful Structures from Climate Extreme Data

Gentine, Pierre [Columbia Univ., New York, NY (United States)] (ORCID:0000000208458345)
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Abstract

The original proposal described an interdisciplinary team spanning UC San Diego (lead), Columbia University, and UC Irvine, with Columbia investigators including Pierre Gentine, Elias Bareinboim, and Marcus van Lier-Walqui. The proposal further specified a leadership structure in which Columbia co-investigators contributed across the three aims, with Co-PI Gentine serving as a point of contact with science teams and with responsibilities distributed across aims.

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42 ENGINEERING54 ENVIRONMENTAL SCIENCES58 GEOSCIENCESmachine learningreduced orderspatio-temporal dataturbulence

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Gentine, Pierre [Columbia Univ., New York, NY (United States)] (ORCID:0000000208458345). 2025-12-31. Discovering Physically Meaningful Structures from Climate Extreme Data. https://doi.org/10.2172/3011342

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DOE OSTI2026

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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.

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