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 problems modeled via Ordinary Differential Equations (ODEs). The proposed method merges the standard PINNs with a functional interpolation technique named Theory of Functional Connections (TFC). In particular, this work focuses on the capability of X-TFC in solving inverse problems to estimate the parameters governing the epidemiological compartmental models via a deterministic approach. The epidemiological compartmental models treated in this work are Susceptible-Infectious-Recovered (SIR), Susceptible-Exposed-Infectious-Recovered (SEIR), and Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS). The results show the low computational times, the high accuracy, and effectiveness of the X-TFC method in performing data-driven parameters’ discovery systems modeled via parametric ODEs using unperturbed and perturbed data.

Future exploration and human missions on large planetary bodies (e.g., moon, Mars) will require advanced guidance navigation and control algorithms for the powered descent phase, which must be capa...

This paper presents a novel framework, combining the indirect method and Physics-Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal planar orbit transfer pro...

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 (PINNs) specifically designed for tackling optimal control problems via the Pontryagin Minimum Principle (PMP) application (e.g., indirect method). The PMP provides first-order necessary optimality conditions, which result in a Two-Point Boundary Value Problem (TPBVP). More precisely, PoNNs learn the optimal control actions from the unknown solutions of the arising TPBVP, modeling them with Neural Networks (NNs). The characteristic feature of PoNNs is the use of PINNs combined with a functional interpolation technique, named the Theory of Functional Connections (TFC), which forms the so-called PINN-TFC based frameworks. According to these frameworks, the unknown solutions are modeled via the TFC’s constrained expressions using NNs as free functions. The results show that PoNNs can be successfully applied to learn optimal controls for the class of optimal intercept problems considered in this paper.

This paper focuses on the use of meta-reinforcement learning for the autonomous guidance of a spacecraft during the terminal phase of an impact mission toward a binary asteroid system. The control ...

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 satellites in the near future, increasing the probability of collisions and debris generation. This paper analyzes the long-term evolution of the LEO ASO population with the goal of estimating LEO orbital capacity. This is carried out by introducing a new probabilistic source–sink model. The developed source–sink model is a multishell multispecies model, which includes different object species, such as active and derelict satellites, and debris. Furthermore, debris are divided into the following two subgroups: trackable and nontrackable debris, the last ones representing a significant hazard for active satellites. In addition, the proposed model accounts for collision events and atmospheric drag effects, which include the influence of solar activity. Indeed, the Jacchia–Bowman 2008 thermospheric density model is exploited. The results prove that considering untracked debris within the model produces more collisions, and therefore a smaller population of active satellites affecting the safety of LEO and its orbital capacity.