11 citations · 20 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2020★ 9 cited
Physics-Informed Extreme Theory of Functional Connections Applied to Data-Driven Parameters Discovery of Epidemiological Compartmental Models
Enrico Schiassi, Andrea D'Ambrosio, Mario De Florio +2
In this work we apply a novel, accurate, fast, and robust physics-informed neural network framework for data-driven parameters discovery of problems modeled via parametric ordinary…
cs.LG2020★ 11 cited
Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations
Enrico Schiassi, Carl Leake, Mario De Florio +3
In this work we present a novel, accurate, and robust physics-informed method for solving problems involving parametric differential equations (DEs) called the Extreme Theory of Fu…