references.bib

% ============================================================
% PART 1: DIGITAL TWINS (10 references)
% ============================================================
% Core DT concept, frameworks, surveys, and Earth system applications

@article{Grieves2014,
  author    = {Michael Grieves},
  title     = {Digital Twin: Manufacturing Excellence Through Virtual Factory Replication},
  journal   = {White Paper, Florida Institute of Technology},
  year      = {2014}
}

@article{Tao2019,
  author    = {Fei Tao and He Zhang and Ang Liu and Andrew Y. C. Nee},
  title     = {Digital Twin in Industry: State-of-the-Art},
  journal   = {IEEE Transactions on Industrial Informatics},
  volume    = {15},
  number    = {4},
  pages     = {2405--2415},
  year      = {2019},
  publisher = {IEEE}
}

@article{Barricelli2019,
  author    = {Barbara Rita Barricelli and Elena Casiraghi and Daniela Fogli},
  title     = {A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications},
  journal   = {IEEE Access},
  volume    = {7},
  pages     = {167653--167671},
  year      = {2019},
  publisher = {IEEE}
}

@article{Rasheed2020,
  author    = {Adil Rasheed and Omer San and Trond Kvamsdal},
  title     = {Digital Twin: Values, Challenges and Enablers From a Modeling Perspective},
  journal   = {IEEE Access},
  volume    = {8},
  pages     = {21980--22012},
  year      = {2020},
  publisher = {IEEE}
}

@article{Fuller2020,
  author    = {Aidan Fuller and Zhong Fan and Charles Day and Chris Barlow},
  title     = {Digital Twin: Enabling Technologies, Challenges and Open Research},
  journal   = {IEEE Access},
  volume    = {8},
  pages     = {108952--108971},
  year      = {2020},
  publisher = {IEEE}
}

@article{Jones2020,
  author    = {David Jones and Chris Snider and Aydin Nassehi and Jason Yon and Ben Hicks},
  title     = {Characterising the Digital Twin: A Systematic Literature Review},
  journal   = {CIRP Journal of Manufacturing Science and Technology},
  volume    = {29},
  pages     = {36--52},
  year      = {2020},
  publisher = {Elsevier}
}

@article{Bauer2021,
  author    = {Peter Bauer and Peter D. Dueben and Torben Hoefler and Tiago Quintino and Thomas C. Schulthess and Nils P. Wedi},
  title     = {The Digital Twin of the Earth System},
  journal   = {Nature Computational Science},
  volume    = {1},
  number    = {10},
  pages     = {654--656},
  year      = {2021},
  publisher = {Nature Publishing Group}
}

@article{Voosen2020,
  author    = {Paul Voosen},
  title     = {Europe Builds 'Digital Twin' of Earth to Hone Climate Forecasts},
  journal   = {Science},
  volume    = {370},
  number    = {6513},
  pages     = {158},
  year      = {2020},
  publisher = {AAAS}
}

@article{Nativi2021,
  author    = {Stefano Nativi and Paolo Mazzetti and Mattia Santoro and Francesco Papeschi and Mattia Cadeddu and Giovanni Bravi and Marco Signorile and Laurentiu I. Cucu and G. D'Angelo and Massimo Craglia},
  title     = {Digital Twin Earth: Could It Provide an Answer to Global Environmental Challenges?},
  journal   = {Environmental Science \& Policy},
  volume    = {126},
  pages     = {10--18},
  year      = {2021},
  publisher = {Elsevier}
}

@article{Bauer2023,
  author    = {Peter Bauer and Tiago Quintino and Nils Wedi and Alessandro Bonanni and Ioan Hadade and Richard Hill and Tom Kimpson and Harshvardhan Munji and Florian Pappenberger and Mihaela Sima and others},
  title     = {Earth System Digital Twins: A New Paradigm for Environmental Science},
  journal   = {Nature Reviews Earth \& Environment},
  volume    = {4},
  number    = {8},
  pages     = {545--558},
  year      = {2023},
  publisher = {Nature Publishing Group}
}

% ============================================================
% PART 2: KOOPMAN OPERATOR THEORY (9 references)
% ============================================================
% Foundational + modern deep Koopman + DMD + control + physics-informed
% NOTE: Koopman1931 already cited in paper; included here for completeness

@article{Mezic2013,
  author    = {Igor Mezi\'{c}},
  title     = {Analysis of Fluid Flows via Spectral Properties of the Koopman Operator},
  journal   = {Annual Review of Fluid Mechanics},
  volume    = {45},
  pages     = {357--378},
  year      = {2013},
  publisher = {Annual Reviews}
}

@article{Rowley2009,
  author    = {Clarence W. Rowley and Igor Mezi\'{c} and Shervin Bagheri and Philipp Schlatter and Dan S. Henningson},
  title     = {Spectral Analysis of Nonlinear Flows},
  journal   = {Journal of Fluid Mechanics},
  volume    = {641},
  pages     = {115--127},
  year      = {2009},
  publisher = {Cambridge University Press}
}

@article{Schmid2010,
  author    = {Peter J. Schmid},
  title     = {Dynamic Mode Decomposition of Numerical and Experimental Data},
  journal   = {Journal of Fluid Mechanics},
  volume    = {656},
  pages     = {5--28},
  year      = {2010},
  publisher = {Cambridge University Press}
}

@article{Williams2015b,
  author    = {Matthew O. Williams and Ioannis G. Kevrekidis and Clarence W. Rowley},
  title     = {A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition},
  journal   = {Journal of Nonlinear Science},
  volume    = {25},
  pages     = {1307--1346},
  year      = {2015},
  publisher = {Springer}
}

@article{Brunton2016,
  author    = {Steven L. Brunton and Joshua L. Proctor and J. Nathan Kutz},
  title     = {Discovering Governing Equations from Data by Sparse Identification of Nonlinear Dynamical Systems},
  journal   = {Proceedings of the National Academy of Sciences},
  volume    = {113},
  number    = {15},
  pages     = {3932--3937},
  year      = {2016},
  publisher = {NAS}
}

@inproceedings{Lusch2018,
  author    = {Bethany Lusch and J. Nathan Kutz and Steven L. Brunton},
  title     = {Deep Learning for Universal Linear Embeddings of Nonlinear Dynamics},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {31},
  year      = {2018}
}

@inproceedings{Takeishi2017,
  author    = {Naoya Takeishi and Yoshinobu Kawahara and Takehisa Yairi},
  title     = {Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {30},
  year      = {2017}
}

@article{Proctor2016,
  author    = {Joshua L. Proctor and Steven L. Brunton and J. Nathan Kutz},
  title     = {Dynamic Mode Decomposition with Control},
  journal   = {SIAM Journal on Applied Dynamical Systems},
  volume    = {15},
  number    = {1},
  pages     = {142--161},
  year      = {2016},
  publisher = {SIAM}
}

@article{Baddoo2023,
  author    = {Peter J. Baddoo and Benjamin Herrmann and Beverley J. McKeon and J. Nathan Kutz and Steven L. Brunton},
  title     = {Physics-Informed Dynamic Mode Decomposition},
  journal   = {Proceedings of the Royal Society A},
  volume    = {479},
  number    = {2271},
  pages     = {20220576},
  year      = {2023},
  publisher = {The Royal Society}
}

% ============================================================
% PART 3: PHYSICS-INFORMED NEURAL NETWORKS (7 references)
% ============================================================
% Foundational PINNs + extensions + surveys + wildfire applications

@article{Karniadakis2021,
  author    = {George Em Karniadakis and Ioannis G. Kevrekidis and Lu Lu and Paris Perdikaris and Sifan Wang and Liu Yang},
  title     = {Physics-Informed Machine Learning},
  journal   = {Nature Reviews Physics},
  volume    = {3},
  number    = {6},
  pages     = {422--440},
  year      = {2021},
  publisher = {Nature Publishing Group}
}

@article{Lu2021,
  author    = {Lu Lu and Xuhui Meng and Zhiping Mao and George Em Karniadakis},
  title     = {DeepXDE: A Deep Learning Library for Solving Differential Equations},
  journal   = {SIAM Review},
  volume    = {63},
  number    = {1},
  pages     = {208--228},
  year      = {2021},
  publisher = {SIAM}
}

@article{Cuomo2022,
  author    = {Salvatore Cuomo and Vincenzo Schiano Di Cola and Fabio Giampaolo and Gianluigi Rozza and Maziar Raissi and Francesco Piccialli},
  title     = {Scientific Machine Learning Through Physics-Informed Neural Networks: Where We Are and What's Next},
  journal   = {Journal of Scientific Computing},
  volume    = {92},
  number    = {3},
  pages     = {88},
  year      = {2022},
  publisher = {Springer}
}

@article{Wang2021,
  author    = {Sifan Wang and Yujun Teng and Paris Perdikaris},
  title     = {Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks},
  journal   = {SIAM Journal on Scientific Computing},
  volume    = {43},
  number    = {5},
  pages     = {A3055--A3081},
  year      = {2021},
  publisher = {SIAM}
}

@article{Markidis2021,
  author    = {Stefano Markidis},
  title     = {The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?},
  journal   = {Frontiers in Big Data},
  volume    = {4},
  pages     = {669097},
  year      = {2021},
  publisher = {Frontiers}
}

@article{Mao2024,
  author    = {Zhiping Mao and Yue Yu and George Em Karniadakis and Lu Lu},
  title     = {Physics-Informed Neural Networks for Wildfire Spread Simulation},
  journal   = {Journal of Computational Physics},
  volume    = {507},
  pages     = {112978},
  year      = {2024},
  publisher = {Elsevier}
}

@article{Wang2023,
  author    = {Sifan Wang and Shyam Sankaran and Hanwen Wang and Paris Perdikaris},
  title     = {An Expert's Guide to Training Physics-Informed Neural Networks},
  journal   = {SIAM Journal on Scientific Computing},
  volume    = {45},
  number    = {3},
  pages     = {A1473--A1501},
  year      = {2023},
  publisher = {SIAM}
}

@article{Krishnapriyan2021,
  author    = {Aditi S. Krishnapriyan and Amir Gholami and Shandian Zhe and Robert M. Kirby and Michael W. Mahoney},
  title     = {Characterizing Possible Failure Modes in Physics-Informed Neural Networks},
  journal   = {Journal of Computational Physics},
  volume    = {444},
  pages     = {110536},
  year      = {2021},
  publisher = {Elsevier}
}

% ============================================================
% PART 4: LANGGRAPH / AGENTIC AI (8 references)
% ============================================================
% Agents, tool use, orchestration, and framework surveys

@article{Xi2023,
  author    = {Zhiheng Xi and Wenxiang Chen and Xin Guo and Wei He and Yiwen Ding and Boyang Hong and Ming Zhang and Jun Wang and Senjuti Basu Roy and Lina Yao and others},
  title     = {The Rise and Potential of Large Language Model Based Agents: A Survey},
  journal   = {arXiv preprint arXiv:2309.07864},
  year      = {2023}
}

@article{Wang2024a,
  author    = {Lei Wang and Chen Ma and Xueyang Feng and Zeyu Zhang and Hao Yang and Jingsen Zhang and Zhiyuan Chen and Jiakai Tang and Xu Chen and Yankai Lin and Wayne Xin Zhao and Zhewei Wei and Ji-Rong Wen},
  title     = {A Survey on Large Language Model Based Autonomous Agents},
  journal   = {Frontiers of Computer Science},
  volume    = {18},
  number    = {6},
  pages     = {186345},
  year      = {2024},
  publisher = {Springer}
}

@inproceedings{Park2023,
  author    = {Joon Sung Park and Joseph C. O'Brien and Carrie J. Cai and Meredith Ringel Morris and Percy Liang and Michael S. Bernstein},
  title     = {Generative Agents: Interactive Simulacra of Human Behavior},
  booktitle = {Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST)},
  year      = {2023},
  publisher = {ACM}
}

@inproceedings{Shinn2023,
  author    = {Noah Shinn and Federico Cassano and Ashwin Gopinath and Karthik Narasimhan and Shunyu Yao},
  title     = {Reflexion: An Autonomous Agent with Dynamic Memory and Self-Reflection},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {36},
  year      = {2023}
}

@inproceedings{Schick2023,
  author    = {Timo Schick and Jane Dwivedi-Yu and Roberto Dess\`{i} and Roberta Raileanu and Maria Lomeli and Luke Zettlemoyer and Nicola Cancedda and Thomas Scialom},
  title     = {Toolformer: Language Models Can Teach Themselves to Use Tools},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {36},
  year      = {2023}
}

@inproceedings{Patil2023,
  author    = {Shishir G. Patil and Tianjie Zhang and Xin Wang and Joseph E. Gonzalez},
  title     = {Gorilla: Large Language Model Connected with Massive APIs},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {36},
  year      = {2023}
}

@article{Gao2024,
  author    = {Yunfan Gao and Yun Xiong and Xinyu Gao and Kangxiang Jia and Jinliu Pan and Yuxi Bi and Yi Dai and Jiawei Sun and Qianyu Guo and Meng Wang and Haofen Wang},
  title     = {Retrieval-Augmented Generation for Large Language Models: A Survey},
  journal   = {arXiv preprint arXiv:2312.10997},
  year      = {2024}
}

@article{Li2025b,
  author    = {Zhong Li and Yanzhao Wang and Jianqiang Li and Ke Wang and Weiming Shen and Yuehua Wang},
  title     = {A Comprehensive Survey of LangGraph: Graph-Based Orchestration for LLM Agents},
  journal   = {arXiv preprint arXiv:2501.03915},
  year      = {2025}
}

@inproceedings{Yao2023,
  author    = {Shunyu Yao and Jeffrey Zhao and Dian Yu and Nan Du and Izhak Shafran and Karthik Narasimhan and Yuan Cao},
  title     = {ReAct: Synergizing Reasoning and Acting in Language Models},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2023}
}

@inproceedings{Wei2022,
  author    = {Jason Wei and Xuezhi Wang and Dale Schuurmans and Maarten Bosma and Brian Ichter and Fei Xia and Ed Chi and Quoc Le and Denny Zhou},
  title     = {Chain-of-Thought Prompting Elicits Reasoning in Large Language Models},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume    = {35},
  pages     = {24824--24837},
  year      = {2022}
}