| Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations E Schiassi, R Furfaro, C Leake, M De Florio, H Johnston, D Mortari Neurocomputing 457, 334-356, 2021 | 162* | 2021 |
| Physics-informed neural networks and functional interpolation for data-driven parameters discovery of epidemiological compartmental models E Schiassi, M De Florio, A D’Ambrosio, D Mortari, R Furfaro Mathematics 9 (17), 2069, 2021 | 62* | 2021 |
| Physics-informed neural networks and functional interpolation for stiff chemical kinetics M De Florio, E Schiassi, R Furfaro Chaos: An Interdisciplinary Journal of Nonlinear Science 32 (6), 2022 | 49 | 2022 |
| Physics-informed neural networks for rarefied-gas dynamics: Thermal creep flow in the Bhatnagar–Gross–Krook approximation M De Florio, E Schiassi, BD Ganapol, R Furfaro Physics of Fluids 33 (4), 2021 | 49 | 2021 |
| Physics-informed neural networks for the point kinetics equations for nuclear reactor dynamics E Schiassi, M De Florio, BD Ganapol, P Picca, R Furfaro Annals of Nuclear Energy 167, 108833, 2022 | 39 | 2022 |
| Solutions of Chandrasekhar’s basic problem in radiative transfer via theory of functional connections M De Florio, E Schiassi, R Furfaro, BD Ganapol, D Mostacci Journal of quantitative spectroscopy and radiative transfer 259, 107384, 2021 | 26 | 2021 |
| Physics-informed extreme theory of functional connections applied to optimal orbit transfer E Schiassi, A D’Ambrosio, H Johnston, M De Florio, K Drozd, R Furfaro, ... Proceedings of the AAS/AIAA Astrodynamics Specialist Conference, Lake Tahoe …, 2020 | 20 | 2020 |
| Physics-Informed Neural Networks for 2nd order ODEs with sharp gradients M De Florio, E Schiassi, F Calabrò, R Furfaro Journal of Computational and Applied Mathematics 436, 115396, 2024 | 19 | 2024 |
| Theory of functional connections applied to linear ODEs subject to integral constraints and linear ordinary integro-differential equations M De Florio, E Schiassi, A D’Ambrosio, D Mortari, R Furfaro Mathematical and computational applications 26 (3), 65, 2021 | 18 | 2021 |
| AI-Aristotle: A physics-informed framework for systems biology gray-box identification N Ahmadi Daryakenari*, M De Florio*, K Shukla, ... PLOS Computational Biology 20 (3), e1011916, 2024 | 15 | 2024 |
| Physics-informed neural networks for rarefied-gas dynamics: Poiseuille flow in the BGK approximation M De Florio, E Schiassi, BD Ganapol, R Furfaro Zeitschrift für angewandte Mathematik und Physik 73 (3), 126, 2022 | 15* | 2022 |
| Grain Size Effects on Visible and Near-infrared (0.35–2.5 μm) Laboratory Spectra of Ordinary Chondrite and HED Meteorites B Bowen, V Reddy, M De Florio, T Kareta, N Pearson, R Furfaro, ... The Planetary Science Journal 4 (3), 52, 2023 | 10 | 2023 |
| Developing a cost-effective emulator for groundwater flow modeling using deep neural operators ML Taccari, H Wang, S Goswami, M De Florio, J Nuttall, X Chen, ... Journal of Hydrology 630, 130551, 2024 | 8 | 2024 |
| AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression M De Florio, IG Kevrekidis, GE Karniadakis Chaos, Solitons & Fractals 188, 115538, 2024 | 7 | 2024 |
| Grain Size Effects on UV–MIR (0.2–14 μm) Spectra of Carbonaceous Chondrite Groups DC Cantillo, V Reddy, A Battle, BNL Sharkey, NC Pearson, T Campbell, ... The Planetary Science Journal 4 (9), 177, 2023 | 4 | 2023 |
| Analysis of biologically plausible neuron models for regression with spiking neural networks M De Florio, A Kahana, GE Karniadakis arXiv preprint arXiv:2401.00369, 2023 | 2 | 2023 |
| Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology M De Florio, Z Zou, DE Schiavazzi, GE Karniadakis ArXiv, 2024 | 1 | 2024 |
| Physics-Informed Neural Networks for 1-D Steady-State Diffusion-Advection-Reaction Equations E Laghi, Laura: Schiassi, M De Florio, R Furfaro, D Mostacci Nuclear Science and Engineering 197 (9), 2373–2403, 2023 | 1 | 2023 |
| Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network M Frankel, M De Florio, E Schiassi, L Sela Engineering Proceedings 69 (1), 40, 2024 | | 2024 |
| The Population of Small Near-Earth Objects: Composition, Source Regions, and Rotational Properties JA Sanchez, V Reddy, A Thirouin, WF Bottke, T Kareta, M De Florio, ... The Planetary Science Journal 5 (6), 131, 2024 | | 2024 |
Roberto FurfaroProfessor of systems and industrial engineering, aerospace and mechanical engineering在 email.arizona.edu 的电子邮件经过验证
