Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations. - Enrico Schiassi; Carl Leake; Mario De Florio; Hunter Johnston; Roberto Furfaro; Daniele Mortari (CoRR, 2020)

Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations. - Enrico Schiassi; Roberto Furfaro; Carl Leake; Mario De Florio; Hunter Johnston; Daniele Mortari (Neurocomputing, 2021)

Physics-Informed Neural Networks and Functional Interpolation for Initial Value Problems with Applications to Integro-Differential and Stiff Differential Equations. - Mario De Florio (2022)

AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression. - Mario De Florio; Ioannis G. Kevrekidis; George Em Karniadakis (CoRR, 2023)

Analysis of biologically plausible neuron models for regression with spiking neural networks. - Mario De Florio; Adar Kahana; George Em Karniadakis (CoRR, 2024)

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology. - Mario De Florio; Zongren Zou; Daniele E. Schiavazzi; George Em Karniadakis (CoRR, 2024)

AI-Aristotle: A physics-informed framework for systems biology gray-box identification. - Nazanin Ahmadi Daryakenari; Mario De Florio; Khemraj Shukla; George Em Karniadakis (PLoS Comput. Biol., 2024)

Physics-Informed Neural Networks for 2nd order ODEs with sharp gradients. - Mario De Florio; Enrico Schiassi; Francesco Calabrò; Roberto Furfaro (J. Comput. Appl. Math., 2024)