Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1692377

Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems

Ramapuram Matavalam, Amarsagar Reddy·Vaidya, Umesh·Ajjarapu, Venkataramana
View original source ↗Read source document ↗Export metadata ↓

Abstract

In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. Here, the uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.

Explore related subjects

42 ENGINEERINGMonte Carlo methodsNonlinear systemsPower system dynamicsProbability density functionReachability analysisRobotsUncertainty

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ramapuram Matavalam, Amarsagar Reddy, Vaidya, Umesh, Ajjarapu, Venkataramana. 2020-07-01. Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems. https://doi.org/10.23919/acc45564.2020.9147295

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↗