Mario De Florio

Postdoctoral Researcher at the National Renewable Energy Laboratory (NREL). Research on Physics-Informed Machine Learning

Journal Papers

  • De Florio, M., Kevrekidis, I.G. and Karniadakis, G.E., 2024. AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression. Chaos, Solitons & Fractals, 188, p.115538. DOI
  • Sanchez, J.A., Reddy, V., Thirouin, A., Bottke, W.F., Kareta, T., De Florio, M., Sharkey, B.N., Battle, A., Cantillo, D.C. and Pearson, N., 2024. The Population of Small Near-Earth Objects: Composition, Source Regions, and Rotational Properties. The Planetary Science Journal, 5(6), p.131. DOI
  • Daryakenari, N. A., De Florio, M., Shukla, K., & Karniadakis, G. E. (2024). AI-Aristotle: A physics-informed framework for systems biology gray-box identification. PLOS Computational Biology, 20(3). DOI
  • Taccari, M.L., Wang, H., Goswami, S., De Florio, M., Nuttall, J., Chen, X. & Jimack, P.K. (2024). Developing a cost-effective emulator for groundwater flow modeling using deep neural operators. Journal of Hydrology, p.130551. DOI
  • Cantillo, D.C., Reddy, V., Battle, A., Sharkey, B.N., Pearson, N.C., Campbell, T., Satpathy, A., De Florio, M., Furfaro, R. & Sanchez, J. (2023). Grain Size Effects on UV–MIR (0.2–14 um) Spectra of Carbonaceous Chondrite Groups. The Planetary Science Journal, 4(9), p.177. DOI
  • De Florio, M., Schiassi, E., Calabrò, F., & Furfaro, R. (2024). Physics-Informed Neural Networks for 2nd order ODEs with sharp gradients. Journal of Computational and Applied Mathematics, 436, 115396. DOI
  • Bowen, B., Reddy, V., De Florio, M., Kareta, T., Pearson, N., Furfaro, R., ... & Battle, A. (2023). Grain Size Effects on Visible and Near-infrared (0.35–2.5 μm) Laboratory Spectra of Ordinary Chondrite and HED Meteorites. The Planetary Science Journal, 4(3), 52. DOI
  • Laghi, L., Schiassi, E., De Florio, M., Furfaro, R., & Mostacci, D. (2023). Physics-Informed Neural Networks for 1-D Steady-State Diffusion-Advection-Reaction Equations, Nuclear Science and Engineering. DOI
  • De Florio, M., Schiassi, E., & Furfaro, R. (2022). Physics-informed neural networks and functional interpolation for stiff chemical kinetics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(6), 063107. DOI
  • De Florio, M., Schiassi, E., Ganapol, B. D., & Furfaro, R. (2022). Physics-Informed Neural Networks for rarefied-gas dynamics: Poiseuille flow in the BGK approximation. Zeitschrift für angewandte Mathematik und Physik, 73(3), 1-18. DOI
  • Schiassi, E., De Florio, M., Ganapol, B. D., Picca, P., & Furfaro, R. (2022). Physics-informed neural networks for the point kinetics equations for nuclear reactor dynamics. Annals of Nuclear Energy, 167, 108833. DOI
  • De Florio, M., Schiassi, E., D’Ambrosio, A., Mortari, D., & Furfaro, R. (2021). Theory of functional connections applied to linear odes subject to integral constraints and linear ordinary integro-differential equations. Mathematical and Computational Applications, 26(3), 65. DOI
  • Schiassi, E., De Florio, M., D’Ambrosio, A., Mortari, D., & Furfaro, R. (2021). Physics-informed neural networks and functional interpolation for data-driven parameters discovery of epidemiological compartmental models. Mathematics, 9(17), 2069. DOI
  • Schiassi, E., Furfaro, R., Leake, C., De Florio, M., Johnston, H., & Mortari, D. (2021). Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations. Neurocomputing, 457, 334-356. DOI
  • De Florio, M., Schiassi, E., Ganapol, B. D., & Furfaro, R. (2021). Physics-informed neural networks for rarefied-gas dynamics: Thermal creep flow in the Bhatnagar–Gross–Krook approximation. Physics of Fluids, 33(4), 047110. DOI
  • De Florio, M., Schiassi, E., Furfaro, R., Ganapol, B. D., & Mostacci, D. (2021). Solutions of Chandrasekhar’s basic problem in radiative transfer via theory of functional connections. Journal of Quantitative Spectroscopy and Radiative Transfer, 259, 107384. DOI

Preprints

  • De Florio, M., Zongren, Z., Schiavazzi, D.E., and Karniadakis, G.E., 2024. Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology. arXiv preprint:2408.07201. arXiv
  • Sanchez, J.A., Reddy, V., Thirouin, A., Bottke, W.F., Kareta, T., \textbf{De Florio, M.}, Sharkey, B.N., Battle, A., Cantillo, D.C. and Pearson, N., 2024. The population of small near-Earth objects: composition, source regions and rotational properties. arXiv preprint:2404.18263. arXiv
  • De Florio, M., Kahana, A. & Karniadakis, G.E. (2023). Analysis of biologically plausible neuron models for regression with spiking neural networks. arXiv preprint:2401.00369. arXiv
  • De Florio, M., Kevrekidis, I.G. & Karniadakis, G.E. (2023). AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression. arXiv preprint:2312.14237. arXiv
  • Daryakenari, N.A., De Florio, M., Shukla, K. & Karniadakis, G.E. (2023). AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification. arXiv preprint:2310.01433. arXiv
  • Schiassi, E., D'Ambrosio, A., De Florio, M., Furfaro, R., & Curti, F. (2020). Physics-informed extreme theory of functional connections applied to data-driven parameters discovery of epidemiological compartmental models. arXiv preprint arXiv:2008.05554. arXiv
  • Schiassi, E., Leake, C., De Florio, M., Johnston, H., Furfaro, R., & Mortari, D. (2020). Extreme theory of functional connections: A physics-informed neural network method for solving parametric differential equations. arXiv preprint arXiv:2005.10632. arXiv

Conference Proceedings

  • Schiassi, E., D’Ambrosio, A., Johnston, H., De Florio, M., Drozd, K., Furfaro, R., ... & Mortari, D. (2020, August). Physics-informed extreme theory of functional connections applied to optimal orbit transfer. In Proceedings of the AAS/AIAA Astrodynamics Specialist Conference, Lake Tahoe, CA, USA (pp. 9-13). RG
  • Sonnett, S., Grav, T., Williamson, B., Witry, J., Reddy, V., Furfaro, R., ... & Bauer, J. (2019, September). Lightcurves, Shape Models, and HG Parameters of Trojan and Hilda Binary Candidates. In EPSC-DPS Joint Meeting 2019 (Vol. 2019, pp. EPSC-DPS2019).