Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Andrea D'Ambrosio"'
Publikováno v:
Mathematics, Vol 12, Iss 9, p 1360 (2024)
This manuscript introduces the first hp-adaptive mesh refinement algorithm for the Theory of Functional Connections (TFC) to solve hypersensitive two-point boundary-value problems (TPBVPs). The TFC is a mathematical framework that analytically satisf
Externí odkaz:
https://doaj.org/article/2b001d4678df4c5b89a99776a5271787
Autor:
Andrea D’Ambrosio, Roberto Furfaro
Publikováno v:
Aerospace, Vol 11, Iss 3, p 228 (2024)
This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal contro
Externí odkaz:
https://doaj.org/article/32136de1b4d84e3a88cb482ee0157bcb
Publikováno v:
Mathematics, Vol 11, Iss 17, p 3635 (2023)
In this manuscript, we explore how the solution of the matrix differential Riccati equation (MDRE) can be computed with the Extreme Theory of Functional Connections (X-TFC). X-TFC is a physics-informed neural network that uses functional interpolatio
Externí odkaz:
https://doaj.org/article/94cb27e611aa4594a9ba01d3420a08ce
Publikováno v:
Mathematical and Computational Applications, Vol 26, Iss 3, p 65 (2021)
This study shows how the Theory of Functional Connections (TFC) allows us to obtain fast and highly accurate solutions to linear ODEs involving integrals. Integrals can be constraints and/or terms of the differential equations (e.g., ordinary integro
Externí odkaz:
https://doaj.org/article/b900655eb61d4266b31dded558aec94e
Publikováno v:
Mathematics, Vol 9, Iss 17, p 2069 (2021)
In this work, we apply a novel and accurate Physics-Informed Neural Network Theory of Functional Connections (PINN-TFC) based framework, called Extreme Theory of Functional Connections (X-TFC), for data-physics-driven parameters’ discovery of probl
Externí odkaz:
https://doaj.org/article/dd5d97b9cdeb46988f4ea5730b014987
Publikováno v:
Aerospace, Vol 8, Iss 7, p 195 (2021)
The problem of real-time optimal guidance is extremely important for successful autonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimizat
Externí odkaz:
https://doaj.org/article/022a732d734a46f0a6e22448ca014d9f
Publikováno v:
Mathematics, Vol 9, Iss 9, p 996 (2021)
In this work, we introduce Pontryagin Neural Networks (PoNNs) and employ them to learn the optimal control actions for unconstrained and constrained optimal intercept problems. PoNNs represent a particular family of Physics-Informed Neural Networks (
Externí odkaz:
https://doaj.org/article/c9532abcaf8b4b3a8b042d254444356e
Publikováno v:
Journal of Spacecraft and Rockets. :1-15
The increasing number of anthropogenic space objects (ASOs) in low Earth orbit (LEO) poses a threat to the safety and sustainability of the space environment. Multiple companies are planning to launch large constellations of hundreds to thousands of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc471c264596b4b59c4ed9aadd29a60f
https://doi.org/10.2514/1.a35579
https://doi.org/10.2514/1.a35579
Publikováno v:
Advances in Space Research. 70:3393-3404
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::099fdea4622dec57ae27de296dc5b37c
https://doi.org/10.1016/j.asr.2022.08.049
https://doi.org/10.1016/j.asr.2022.08.049
Autor:
Lorenzo Federici, Andrea Scorsoglio, Luca Ghilardi, Andrea D’Ambrosio, Boris Benedikter, Alessandro Zavoli, Roberto Furfaro
Publikováno v:
Journal of Guidance, Control, and Dynamics. 45:2013-2028
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dfe85aee61d8404bb9f9ca6a0664a04
https://doi.org/10.2514/1.g006832
https://doi.org/10.2514/1.g006832