Roberto Furfaro

Roberto Furfaro is currently Professor at the Department of Systems and Industrial Engineering, Department of Aerospace and Mechanical Engineering, University of Arizona. He is the Director of the Space Systems Engineering Laboratory (SSEL), the Director of the Space Situational Awareness Arizona (SSA-Arizona) Initiative, and currently the PI of the AFRL Cooperative Agreement. He published more than 50 peer-reviewed journal papers and more than 200 conference papers and abstracts. He is technical member of the AIAA Astrodynamics Committee and of the AAS Space Surveillance Committee. In 2010-2016, he was the systems engineering lead for the Science Processing and Operations Center of the NASA OSIRIS REx Asteroid Sample Return Mission. He is currently the lead for the target follow-up team of the recently selected NASA NEO Surveyor Mission. For his contribution to space missions, the asteroid 2003 WX3 was renamed 133474 Roberto Furfaro.

Degrees

• PhD, Aerospace Engineering, The University of Arizona (2004)
• MSc, Aerospace Engineering, Sapienza University of Rome (1998)

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
• 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. DOI: https://doi.org/10.1016/j.jqsrt.2020.107384
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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 
• 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
• 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
• 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
• Scorsoglio, A., Furfaro, R., Linares, R., & Massari, M. (2019). Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit. In 29th AAS/AIAA Space Flight Mechanics Meeting (pp. 1-20).  PDF
• Campbell, T., Reddy, V., Furfaro, R., & Tucker, S. (2019). Characterizing LEO Objects using Simultaneous Multi-Color Optical Array. amos, 51.
• Scorsoglio, A., & Furfaro, R. (2019), ELM-based Actor-Critic Approach to Lyapunov Vector Fields Relative Motion Guidance in Near-Rectilinear Orbits. Conference: 2019 AAS/AIAA Astrodynamics Specialist Conference. Portland, ME, USA. PDF
• 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
• 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
• Drozd K., 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
• 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
• 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
• 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
• 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