Physics-Informed Neural Networks and Functional Interpolation for Data-Driven Parameters Discovery of Epidemiological Compartmental Models
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DOI: 10.3390/math9172069
Abstract: In this work, we apply a novel and accurate Physics-Informed Neural Network Theory of Functional Connections (PINN-TFC) based framework, called Extreme Theory of Functional Connections (X-TFC), for data-physics-driven parameters’ discovery of problems modeled via Ordinary… read more here.
Keywords: compartmental models; parameters discovery; epidemiological compartmental; physics informed ... See more keywords