Thesis/thesis.bib

@inbook{Eichner2003,
  author    = {Eichner, Martin and Kretzschmar, Mirjam},
  pages     = {81--93},
  publisher = {Springer Berlin Heidelberg},
  title     = {Mathematische Modelle in der Infektionsepidemiologie},
  year      = {2003},
  isbn      = {9783642556128},
  booktitle = {Infektionsepidemiologie},
  doi       = {10.1007/978-3-642-55612-8_8}
}

@article{1927,
  author    = {William Ogilvy Kermack and A. G. McKendrick},
  journal   = {Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character},
  title     = {A contribution to the mathematical theory of epidemics},
  year      = {1927},
  issn      = {2053-9150},
  month     = aug,
  number    = {772},
  pages     = {700--721},
  volume    = {115},
  doi       = {10.1098/rspa.1927.0118},
  publisher = {The Royal Society}
}

@article{Foerster2024,
  author    = {Foerster, Dietrich and Bhatkar, Sayali and Bhanot, Gyan},
  title     = {Parametrization of Worldwide Covid-19 data for multiple variants: How is the SAR-Cov2 virus evolving?},
  year      = {2024},
  month     = apr,
  doi       = {10.1101/2024.04.09.24305557},
  publisher = {Cold Spring Harbor Laboratory}
}

@misc{Shaier2021,
  author    = {Shaier, Sagi and Raissi, Maziar and Seshaiyer, Padmanabhan},
  title     = {Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks},
  year      = {2021},
  copyright = {Creative Commons Attribution 4.0 International},
  doi       = {10.48550/ARXIV.2110.05445},
  keywords  = {Machine Learning (cs.LG), Quantitative Methods (q-bio.QM), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Biological sciences, FOS: Biological sciences},
  publisher = {arXiv}
}

@article{Jayatilaka2022,
  author    = {Jayatilaka, R. and Patel, R. and Brar, M. and Tang, Y. and Jisrawi, N.M. and Chishtie, F. and Drozd, J. and Valluri, S.R.},
  journal   = {Materials Today: Proceedings},
  title     = {A mathematical model of COVID-19 transmission},
  year      = {2022},
  issn      = {2214-7853},
  pages     = {101--112},
  volume    = {54},
  doi       = {10.1016/j.matpr.2021.11.480},
  publisher = {Elsevier BV}
}

@book{Zill1997,
  author    = {Dennis G. Zill},
  editor    = {Gary Ostedt},
  publisher = {Brooks/Cole Publishing Company, a division of Internaltional Thomson Publishing Inc.},
  title     = {A First Course In Differential Equations With Modelling Applications},
  year      = {1997},
  edition   = {6}
}

@book{Rudin2007,
  author    = {Walter Rudin},
  publisher = {Oldenbourg Wissenschaftsverlag GmbH},
  title     = {Analysis},
  year      = {2007}
}

@book{Tenenbaum1985,
  author    = {Morris Tenenbaum and Harry Pollard},
  publisher = {Harper and Row, Publishers, Inc.},
  title     = {Ordinary Differential Equations},
  year      = {1985}
}

@Book{EdelsteinKeshet2005,
  author    = {Leah Edelstein-Keshet},
  publisher = {Society for Industrial and Applied Mathematics},
  title     = {Mathematical Models in Biology},
  year      = {2005},
}

@Book{Anderson1991,
  author    = {Anderson, Roy Malcolm; May, Robert M.},
  publisher = {Oxford University Press},
  title     = {Infectious diseases of humans : dynamics and control},
  year      = {1991},
}

@Article{Millevoi2023,
  author    = {Millevoi, Caterina and Pasetto, Damiano and Ferronato, Massimiliano},
  title     = {A Physics-Informed Neural Network approach for compartmental epidemiological models},
  year      = {2023},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International},
  doi       = {10.48550/ARXIV.2311.09944},
  keywords  = {Numerical Analysis (math.NA), FOS: Mathematics, FOS: Mathematics},
}

@Book{Goodfellow-et-al-2016,
  author    = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  publisher = {MIT Press},
  title     = {Deep Learning},
  year      = {2016},
  note      = {\url{http://www.deeplearningbook.org}},
}

@Article{Lagaris1997,
  author    = {Lagaris, I. E. and Likas, A. and Fotiadis, D. I.},
  title     = {Artificial Neural Networks for Solving Ordinary and Partial Differential Equations},
  year      = {1997},
  copyright = {Assumed arXiv.org perpetual, non-exclusive license to distribute this article for submissions made before January 2004},
  doi       = {10.48550/ARXIV.PHYSICS/9705023},
  keywords  = {Computational Physics (physics.comp-ph), Cellular Automata and Lattice Gases (nlin.CG), Quantum Physics (quant-ph), FOS: Physical sciences, FOS: Physical sciences},
}

@Article{Hornik1989,
  author    = {Hornik, Kurt and Stinchcombe, Maxwell and White, Halbert},
  journal   = {Neural Networks},
  title     = {Multilayer feedforward networks are universal approximators},
  year      = {1989},
  issn      = {0893-6080},
  month     = jan,
  number    = {5},
  pages     = {359--366},
  volume    = {2},
  doi       = {10.1016/0893-6080(89)90020-8},
  publisher = {Elsevier BV},
}

@Article{Rosenblatt1958,
  author    = {Rosenblatt, F.},
  journal   = {Psychological Review},
  title     = {The perceptron: A probabilistic model for information storage and organization in the brain.},
  year      = {1958},
  issn      = {0033-295X},
  number    = {6},
  pages     = {386--408},
  volume    = {65},
  doi       = {10.1037/h0042519},
  publisher = {American Psychological Association (APA)},
}

@Book{Minsky1972,
  author    = {Minsky, Marvin and Papert, Seymour A.},
  publisher = {The MIT Press},
  title     = {Perceptrons},
  year      = {1972},
  address   = {Cambridge/Mass. [u.a.]},
  edition   = {2. print. with corr},
  isbn      = {9780262630221},
  note      = {Literaturangaben},
  pagetotal = {258},
  ppn_gvk   = {223298433},
  subtitle  = {An introduction to computational geometry},
}

@Misc{Raissi2017,
  author    = {Raissi, Maziar and Perdikaris, Paris and Karniadakis, George Em},
  title     = {Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations},
  year      = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license},
  doi       = {10.48550/ARXIV.1711.10561},
  keywords  = {Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Numerical Analysis (math.NA), Dynamical Systems (math.DS), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
  publisher = {arXiv},
}

@Book{Demtroeder2021,
  author    = {Demtröder, Wolfgang},
  publisher = {Springer Spektrum},
  title     = {Experimentalphysik 1},
  year      = {2021},
  address   = {Berlin},
  edition   = {9. Auflage},
  isbn      = {978-3-662-62727-3},
  note      = {Auf dem Umschlag: Mit über 2,5 h Lösungsvideos zu ausgewählten Aufgaben},
  series    = {Lehrbuch},
  volume    = {1},
  pagetotal = {507},
  ppn_gvk   = {176791136X},
  subtitle  = {Wolfgang},
}

@Article{Schroedinger1926,
  author    = {Schrödinger, E.},
  journal   = {Physical Review},
  title     = {An Undulatory Theory of the Mechanics of Atoms and Molecules},
  year      = {1926},
  issn      = {0031-899X},
  month     = dec,
  number    = {6},
  pages     = {1049--1070},
  volume    = {28},
  doi       = {10.1103/physrev.28.1049},
  publisher = {American Physical Society (APS)},
}

@Book{Oksendal2000,
  author    = {Oksendal, Bernt},
  publisher = {Springer Berlin / Heidelberg},
  title     = {Stochastic Differential Equations},
  year      = {2000},
  address   = {Berlin, Heidelberg},
  edition   = {5th ed.},
  isbn      = {3-540-63720-6},
  note      = {Description based on publisher supplied metadata and other sources.},
  series    = {Universitext Ser.},
  pagetotal = {1332},
  ppn_gvk   = {1763063550},
  subtitle  = {An Introduction with Applications},
}

@Article{Matsumoto1984,
  author    = {Matsumoto, T.},
  journal   = {IEEE Transactions on Circuits and Systems},
  title     = {A chaotic attractor from Chua’s circuit},
  year      = {1984},
  issn      = {0098-4094},
  month     = dec,
  number    = {12},
  pages     = {1055--1058},
  volume    = {31},
  doi       = {10.1109/tcs.1984.1085459},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
}

@Article{Rumelhart1986,
  author    = {Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J.},
  journal   = {Nature},
  title     = {Learning representations by back-propagating errors},
  year      = {1986},
  issn      = {1476-4687},
  month     = oct,
  number    = {6088},
  pages     = {533--536},
  volume    = {323},
  doi       = {10.1038/323533a0},
  publisher = {Springer Science and Business Media LLC},
}

@Article{Smirnova2017,
  author    = {Smirnova, Alexandra and deCamp, Linda and Chowell, Gerardo},
  journal   = {Bulletin of Mathematical Biology},
  title     = {Forecasting Epidemics Through Nonparametric Estimation of Time-Dependent Transmission Rates Using the SEIR Model},
  year      = {2017},
  issn      = {1522-9602},
  month     = may,
  number    = {11},
  pages     = {4343--4365},
  volume    = {81},
  doi       = {10.1007/s11538-017-0284-3},
  publisher = {Springer Science and Business Media LLC},
}

@Article{Berkhahn2022,
  author    = {Berkhahn, Sarah and Ehrhardt, Matthias},
  journal   = {Advances in Continuous and Discrete Models},
  title     = {A physics-informed neural network to model COVID-19 infection and hospitalization scenarios},
  year      = {2022},
  issn      = {2731-4235},
  month     = oct,
  number    = {1},
  volume    = {2022},
  doi       = {10.1186/s13662-022-03733-5},
  publisher = {Springer Science and Business Media LLC},
}

@Article{Long2021,
  author    = {Long, Jie and Khaliq, A. Q. M. and Furati, K. M.},
  journal   = {International Journal of Computer Mathematics},
  title     = {Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach},
  year      = {2021},
  issn      = {1029-0265},
  month     = may,
  pages     = {1--16},
  doi       = {10.1080/00207160.2021.1929942},
  publisher = {Informa UK Limited},
}

@Article{Olumoyin2021,
  author    = {Olumoyin, K. D. and Khaliq, A. Q. M. and Furati, K. M.},
  journal   = {Epidemiologia},
  title     = {Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate},
  year      = {2021},
  issn      = {2673-3986},
  month     = sep,
  number    = {4},
  pages     = {471--489},
  volume    = {2},
  doi       = {10.3390/epidemiologia2040033},
  publisher = {MDPI AG},
}

@Comment{jabref-meta: databaseType:bibtex;}