System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
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Publication:2221739
DOI10.1007/s00466-020-01894-2zbMath1465.92132arXiv2007.00865OpenAlexW3048608718WikidataQ113326877 ScholiaQ113326877MaRDI QIDQ2221739
Gregory H. Teichert, Krishna Garikipati, Zhongxin Wang, M. Carrasco-Teja, Xiaoxuan Zhang
Publication date: 2 February 2021
Published in: Computational Mechanics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2007.00865
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Uses Software
Cites Work
- Numerical modelling of an SIR epidemic model with diffusion
- Multilayer feedforward networks are universal approximators
- Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
- Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions
- Variational system identification of the partial differential equations governing the physics of pattern-formation: inference under varying fidelity and noise
- Localized outbreaks in an S-I-R model with diffusion
- The Mathematics of Infectious Diseases