Recent Publications

  • De Florio, M., Schiassi, E., and Furfaro, R., Physics-informed neural networks and functional interpolation for stiff chemical kinetics, Chaos 32, 063107 (2022) https://doi.org/10.1063/5.0086649

  • De Florio, M., Schiassi, E., Ganapol, B.D. et al. Physics-Informed Neural Networks for rarefied-gas dynamics: Poiseuille flow in the BGK approximation. Z. Angew. Math. Phys. 73, 126 (2022). https://doi.org/10.1007/s00033-022-01767-z
  • Schiassi, E., De Florio, M., Ganapol, B.D., Picca, P. and Furfaro, R., 2021. Physics-informed neural networks for the point kinetics equations for nuclear reactor dynamics. Annals of Nuclear Energy, p. 108833. DOI: https://doi.org/10.1016/j.anucene.2021.108833
  • De Florio, M., Schiassi, E., D’Ambrosio, A., Mortari, D. and Furfaro, R., 2021. Theory of Functional Connections Applied to Linear ODEs Subject to Integral Constraints and Linear Ordinary Integro-Differential Equations. Mathematics26(3), p.65. DOI: https://doi.org/10.3390/mca26030065
  • Schiassi, E., De Florio, M., D’Ambrosio, A., Mortari, D. and Furfaro, R., 2021. Physics-Informed Neural Networks and Functional Interpolation for Data-Driven Parameters Discovery of Epidemiological Compartmental Models. Mathematics9(17), p.2069. DOI: https://doi.org/10.3390/math9172069
  • Drozd, K., Furfaro, R., Schiassi, E., Johnston, H. and Mortari, D., 2021. Energy-optimal trajectory problems in relative motion solved via Theory of Functional Connections. Acta Astronautica182, pp.361-382. DOI: https://doi.org/10.1016/j.actaastro.2021.01.031
  • P. Picca, R. Furfaro, Multi-generation point kinetics for subcritical systems, Annals of Nuclear Energy (2021). DOI: https://doi.org/10.1016/j.anucene.2021.108527
  • E. Schiassi, R. Furfaro, C. Leake, M. De Florio, H. Johnston, D. Mortari, Extreme Theory of Functional Connections: A Fast Physics-Informed Neural Network Method for Solving Ordinary and Partial Differential Equations, Neurocomputing (2021). DOI: https://doi.org/10.1016/j.neucom.2021.06.015
  • De Florio, M., Schiassi, E., Ganapol, B.D., Furfaro, R., "Physics-informed neural networks for rarefied-gas dynamics: Thermal creep flow in the Bhatnagar–Gross–Krook approximation", Physics of Fluids 33, 047110 (2021). DOI:  https://doi.org/10.1063/5.0046181
  • De Florio, M., Schiassi, E., Furfaro, R., Ganapol, B.D., Mostacci, D. (2020). Solutions of Chandrasekhar’s Basic Problem in Radiative Transfer via Theory of Functional Connections. Journal of Quantitative Spectroscopy & Radiative Transfer. p.107384. DOI: https://doi.org/10.1016/j.jqsrt.2020.107384
  • 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:2008.05554v1. PDF
  • Schiassi, E., D'Ambrosio, A., Johnston, H., De Florio, M., Furfaro, R., Curti, F., Mortari, D., Physics-Informed Extreme Theory of Functional Connections Applied to Optimal Orbit Transfer. AAS 20-524, AAS/AIAA Astrodynamics Specialist Conference, Lake Tahoe, CA, August 9-13, 2020. PDF
  • 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 :2005.10632. PDF