Physics-informed adaptive deep Koopman operator modeling for autonomous vehicle dynamics
Authors:
- Jianhua ZhangJZJianhua Zhang
- Hao ChenHCHao Chen
- Yansong HeYHYansong He
- 26 December 2025
- journal article
- Published by Elsevier in Advanced Engineering Informatics
- Vol. 71
Abstract
No abstract availableThis publication has 35 references indexed in Scilit:
- A survey of autonomous driving frameworks and simulatorsAdvanced Engineering Informatics, 2024
- Large models for intelligent transportation systems and autonomous vehicles: A surveyAdvanced Engineering Informatics, 2024
- Data-driven fault detection and isolation of nonlinear systems using deep learning for Koopman operatorISA Transactions, 2022
- Physics-informed neural networks (PINNs) for fluid mechanics: a reviewActa Mechanica Sinica, 2021
- Real-time transient stability prediction and coherency identification in power systems using Koopman mode analysisElectric Power Systems Research, 2021
- Deep learning nonlinear multiscale dynamic problems using Koopman operatorJournal of Computational Physics, 2021
- Data-driven Koopman operator approach for computational neuroscienceAnnals of Mathematics and Artificial Intelligence, 2019
- Extending Data-Driven Koopman Analysis to Actuated SystemsIFAC-PapersOnLine, 2016
- Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squaresJournal of Power Sources, 2015
- A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode DecompositionJournal of Nonlinear Science, 2015