All 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. Mathematics, 26(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. Mathematics, 9(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 Astronautica, 182, 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
  • Gaudet, B., Furfaro, R., Linares, R., & Scorsoglio, A. (2021). Reinforcement Metalearning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop. Journal of Spacecraft and Rockets, 58(2), 386-399. DOI: https://doi.org/10.2514/1.A34841
  • Schiassi, E., D’Ambrosio, A., Scorsoglio, A., Furfaro, R., & Curti, F. "Class of Optimal Space Guidance Problems Solved via Indirect Methods and Physics-Informed Neural Networks", Conference: 31st AAS/AIAA Space Flight Mechanics Meeting, Virtual, PDF

  • 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

  • Scorsoglio, A., D'Ambrosio, A., Ghilardi, L., Furfaro, R., Gaudet, B., Linares, R., Curti, F. (2020), "Safe lunar landing via images: a reinforcement meta-learning application to autonomous hazard avoidance and landing", Conference: 2020 AAS/AIAA Astrodynamics Specialist Conference - Lake Tahoe (Virtual)

  • Ghilardi, L., D'Ambrosio, A., Scorsoglio, A., Furfaro, R., Linares, R., Curti, F. (2020), "Image-based Optimal Powered Descent Guidance via Deep Recurrent Imitation Learning", Conference: 2020 AAS/AIAA Astrodynamics Specialist Conference - Lake Tahoe (Virtual)

  • 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
  • Doerr, B. G., Linares, R., & Furfaro, R. (2020). Space Objects Maneuvering Prediction via Maximum Causal Entropy Inverse Reinforcement Learning. In AIAA Scitech 2020 Forum (p. 0235). DOI: https://doi.org/10.2514/6.2020-0235 
  • Gaudet, B., Furfaro, R., & Linares, R. (2020). Reinforcement learning for angle-only intercept guidance of maneuvering targets. Aerospace Science and Technology, 99, 105746. DOI: https://doi.org/10.1016/j.ast.2020.105746 
  • Gaudet, B., Linares, R., & Furfaro, R. (2020). Six degree-of-freedom body-fixed hovering over unmapped asteroids via LIDAR altimetry and reinforcement meta-learning. Acta Astronautica. DOI: https://doi.org/10.1016/j.actaastro.2020.03.026 
  • Furfaro, R., Linares, R., & Reddy, V. Space Debris Identification and Characterization via Deep Meta-Learning. PDF
  • Gaudet, B., Linares, R., & Furfaro, R. (2020). Six Degree-of-Freedom Hovering over an Asteroid with Unknown Environmental Dynamics via Reinforcement Learning. In AIAA Scitech 2020 Forum (p. 0953). DOI: https://doi.org/10.2514/6.2020-0953 
  • Gaudet, B., Linares, R., & Furfaro, R. (2020). Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Landing. Advances in Space Research. DOI: https://doi.org/10.1016/j.asr.2019.12.030
  • Gaudet, B., Linares, R., & Furfaro, R. (2020). Terminal adaptive guidance via reinforcement meta-learning: Applications to autonomous asteroid close-proximity operations. Acta Astronautica. PDF
  • Gaudet, B., Furfaro, R., Linares, R., & Scorsoglio, A. (2020). Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop. arXiv preprint arXiv:2004.09978. PDF
  • Furfaro, R., Scorsoglio, A., Linares, R., & Massari, M. (2020). Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach. Acta Astronautica. DOI: http://dx.doi.org/10.1016/j.actaastro.2020.02.051
  • Watson, C. S., Kargel, J. S., Shugar, D. H., Haritashya, U. K., Schiassi, E., & Furfaro, R. (2020). Mass loss from calving in Himalayan proglacial lakes. Frontiers in Earth Science, 7. DOI: http://dx.doi.org/10.3389/feart.2019.00342
  • Furfaro, R., & Mortari, D. (2020). Least-squares solution of a class of optimal space guidance problems via theory of connections. Acta Astronautica, 168, 92-103. DOI: https://doi.org/10.1016/j.actaastro.2019.05.050
  • Holt, H., Armellin, R., Scorsoglio, A., & Furfaro, R. (2020). Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters. In AIAA Scitech 2020 Forum (p. 1694). DOI: https://doi.org/10.2514/6.2020-1694
  • Scorsoglio, A., Furfaro, R., Linares, R., & Gaudet, B. (2020). Image-based Deep Reinforcement Learning for Autonomous Lunar Landing. In AIAA Scitech 2020 Forum (p. 1910). DOI: http://dx.doi.org/10.2514/6.2020-1910
  • Johnston, H., Schiassi, E., Furfaro, R., & Mortari, D. (2020). Fuel-Efficient Powered Descent Guidance on Large Planetary Bodies via Theory of Functional Connections. arXiv preprint arXiv:2001.03572. PDF
  • Cambioni, S., Carter, L. M., Haynes, M., Asphaug, E., & Furfaro, R. (2019). Machine Learning for Characterizing Shallow Subsurface Ice via Radar-Thermal Data Fusion: Validation at Lake Vostok, East Antarctica. EPSC, 2019, EPSC-DPS2019.
  • Gaudet, B., Linares, R., & Furfaro, R. (2019). Six Degree-of-Freedom Hovering using LIDAR Altimetry via Reinforcement Meta-Learning. arXiv preprint arXiv:1911.08553. PDF
  • Noonan, J. W., Reddy, V., Harris, W. M., Bottke, W. F., Sanchez, J. A., Furfaro, R., ... & Nallapu, R. T. (2019). Search for the H Chondrite Parent Body among the Three Largest S-type Asteroids:(3) Juno,(7) Iris, and (25) Phocaea. The Astronomical Journal, 158(5), 213. https://doi.org/10.3847/1538-3881/ab4813 
  • Scorsoglio, A., & Furfaro, R. (2019). ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbit. In 2019 AAS/AIAA Astrodynamics Specialists Conference (pp. 1-20). PDF
  • Bishop, M. P., Young, B. W., Colby, J. D., Furfaro, R., Schiassi, E., & Chi, Z. (2019). Theoretical Evaluation of Anisotropic Reflectance Correction Approaches for Addressing Multi-Scale Topographic Effects on the Radiation-Transfer Cascade in Mountain Environments. Remote Sensing, 11(23), 2728. DOI: http://dx.doi.org/10.3390/rs11232728
  • Picca, P., & Furfaro, R. (2019). A quasi-static approach for the solution of steady-state linear transport problems. Annals of Nuclear Energy, 133, 805-815. DOI: https://doi.org/10.1016/j.anucene.2019.07.015
  • Furfaro, R., Linares, R., & Reddy, V. (2019). Shape Identification of Space Objects via Light Curve Inversion using Deep Learning Models. In AMOS Technologies Conference, Maui Economic Development Board, Kihei, Maui, HI. PDF
  • Campbell, T., Reddy, V., Furfaro, R., & Tucker, S. (2019). Characterizing LEO Objects using Simultaneous Multi-Color Optical Array. amos, 51.
  • Schiassi, E., Furfaro, R., Kargel, J. S., Watson, C. S., Shugar, D. H., & Haritashya, U. K. (2019). GLAM Bio-Lith RT: A Tool for Remote Sensing Reflectance Simulation and Water Components Concentration Retrieval in Glacial Lakes. Frontiers in Earth Science, 7. DOI: http://dx.doi.org/10.3389/feart.2019.00267
  • Drozd K., Furfaro R., & Mortari D. (2019). Constrained Energy-Optimal Guidance in Relative Motion via Theory of Functional Connections and Rapidly-Explored Random Trees. Conference: 2019 AAS/AIAA Astrodynamics Specialist Conference. Portland, ME, USA. PDF
  • Drozd K., Furfaro R., & Lambert D. (2019). Modeling Imaging Uncertainty for OSIRIS-REx's Asteroid Approach Observations. Conference: 29th AAS/AIAA Space Flight Mechanics Meeting. Ka'anapali, HI. PDF
  • Schiassi E., Furfaro R., Johnston H., & Mortari D. (2019). Fuel-efficient Powered Descent Guidance on Planetary Bodies via Theory of Functional Connection 1: Solution of the Equations of Motion, AAS 19-718, AAS/AIAA Astrodynamics Specialist Conference, Portland, ME. PDF
  • Scorsoglio, A., Furfaro, R., Linares, R., & Massari, M. (2019, February). Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit. In 29th AAS/AIAA Space Flight Mechanics Meeting (pp. 1-20). San Diego, CA: American Astronautical Soc.. PDF
  • Drozd, K. M., Furfaro, R., & Topputo, F. (2018). Application of ZEM/ZEV guidance for closed-loop transfer in the Earth-Moon System. In 2018 Space Flight Mechanics Meeting (p. 0958). DOI: http://dx.doi.org/10.2514/6.2018-0958.c1
  • Furfaro, R., Linares, R., & Reddy, V. (2018). Space objects classification via light-curve measurements: deep convolutional neural networks and model-based transfer learning. In AMOS Technologies Conference, Maui Economic Development Board. DOI: https://doi.org/10.1007/s40295-019-00208-w
  • Jiang, X., Furfaro, R., & Li, S. (2018). Integrated guidance for mars entry and powered descent using reinforcement learning and gauss pseudospectral method. In 4th IAA Conference on Dynamics and Control of Space Systems, DYCOSS 2018 (pp. 761-774). Univelt Inc.. DOI: https://doi.org/10.1016/j.actaastro.2018.12.033
  • Furfaro, R., Bloise, I., Orlandelli, M., Di Lizia, P., Topputo, F., & Linares, R. (2018). Deep learning for autonomous lunar landing. In 2018 AAS/AIAA Astrodynamics Specialist Conference (pp. 1-22). PDF
  • Picca, P., & Furfaro, R. (2018). Reactivity determination using the hybrid transport point kinetics and the area method. Annals of Nuclear Energy, 114, 191-197. DOI: https://doi.org/10.1016/j.anucene.2017.12.019
  • Picca, P., & Furfaro, R. (2017). Application of the Transport-Driven Diffusion Approach for Criticality Calculations. Journal of Computational and Theoretical Transport, 46(4), 258-282. DOI: 10.1080/23324309.2017.1352515
  • Picca, P., & Furfaro, R. (2017). Application of Extreme Learning Machines to inverse neutron kinetics. Annals of Nuclear Energy, 100, 1-8. DOI: 10.1016/j.anucene.2016.08.031
  • Campbell, T., Furfaro, R., Linares, R., & Gaylor, D. (2017, January). A deep learning approach for optical autonomous planetary relative terrain navigation. In 27th AAS/AIAA Space Flight Mechanics Meeting, 2017 (pp. 3293-3302). Univelt Inc. PDF
  • Schiassi, E., Furfaro, R., & Mostacci, D. (2016). Bayesian inversion of coupled radiative and heat transfer models for asteroid regoliths and lakes. Radiation Effects and Defects in Solids, 171(9-10), 736-745. DOI: http://dx.doi.org/10.1080/10420150.2016.1253091
  • Schiassi, E., Furfaro, R., & Mostacci, D. (2016). Bayesian inversion of coupled radiative and heat transfer models for asteroid regoliths and lakes. Radiation Effects and Defects in Solids, 171(9-10), 736-745. DOI: http://dx.doi.org/10.1080/10420150.2016.1253091
  • Picca, P., Furfaro, R., & Ganapol, B. D. (2016). Application of non-linear extrapolations for the convergence acceleration of source iteration. Journal of Computational and Theoretical Transport, 45(5), 351-367. DOI: 10.1080/23324309.2016.1167742
  • Picca, P., & Furfaro, R. (2015). Closed-form solution of the first-order Transport-Driven Diffusion approximation. Annals of Nuclear Energy, 76, 431-438. DOI: 10.1016/j.anucene.2014.10.016
  • Picca, P., & Furfaro, R. (2014). A hybrid method for the solution of linear Boltzmann equation. Annals of Nuclear Energy, 72, 214-236. DOI: 10.1016/j.anucene.2014.05.014
  • Furfaro, R., Previti, A., Picca, P., Kargel, J. S., & Bishop, M. P. (2014). Radiative transfer modeling in the cryosphere. In Global Land Ice Measurements from Space (pp. 53-73). Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-540-79818-7_3
  • Picca, P., & Furfaro, R. (2014). Hybrid-transport point kinetics for initially-critical multiplying systems. Progress in Nuclear Energy, 76, 232-243. DOI: 10.1016/j.pnucene.2014.05.013
  • Picca, P., Furfaro, R., & Ganapol, B. D. (2013). A highly accurate technique for the solution of the non-linear point kinetics equations. Annals of Nuclear Energy, 58, 43-53. DOI: 10.1016/j.anucene.2013.03.004
  • Picca, P., & Furfaro, R. (2013). Analytical discrete ordinate method for radiative transfer in dense vegetation canopies. Journal of Quantitative Spectroscopy and Radiative Transfer, 118, 60-69. DOI: 10.1016/j.jqsrt.2012.12.007
  • Picca, P., & Furfaro, R. (2012). Neutron inverse kinetics via Gaussian Processes. Annals of Nuclear Energy, 47, 146-154. DOI: 10.1016/j.anucene.2012.03.023
  • Picca, P., Furfaro, R., & Ganapol, B. D. (2012). On radiative transfer in dense vegetation canopies. Transport Theory and Statistical Physics, 41(3-4), 223-244. DOI: 10.1080/00411450.2012.671218
  • Picca, P., Furfaro, R., & Ganapol, B. D. (2012). An efficient multiproblem strategy for accurate solutions of linear particle transport problems in spherical geometry. Nuclear science and engineering, 170(2), 103-124. DOI: 10.13182/NSE11-05
  • Picca, P., Furfaro, R., & Ganapol, B. D. (2012). Derivation of a physically based hybrid technique for the solution of source-driven time-dependent linear Boltzmann equations. Transport Theory and Statistical Physics, 41(1-2), 23-39. DOI: 10.1080/00411450.2012.671219
  • Picca, P., Furfaro, R., & Ganapol, B. D. (2011). A Hybrid Transport Point-Kinetic method for simulating source transients in subcritical systems. Annals of Nuclear Energy, 38(12), 2680-2688. DOI: 10.1016/j.anucene.2011.08.005
  • Previti, A., Furfaro, R., Picca, P., Ganapol, B. D., & Mostacci, D. (2011). Solving radiative transfer problems in highly heterogeneous media via domain decomposition and convergence acceleration techniques. Applied Radiation and Isotopes, 69(8), 1146-1150. DOI: 10.1016/j.apradiso.2010.11.016
  • Picca, P., Furfaro, R., Kargel, J., & Ganapol, B. D. (2008, April). Forward and inverse models for photon transport in soil-ice mixtures and their application to the problem of retrieving optical properties of planetary surfaces. In Space Exploration Technologies (Vol. 6960, p. 69600O). International Society for Optics and Photonics. DOI: 10.1117/12.777479