activity
20182022
most citedAdaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach

109 citations · 215 across the 10 of their papers we have counts for

collaborators

19 papers

cs.RO2022

Line of Sight Curvature for Missile Guidance using Reinforcement Meta-Learning

Brian Gaudet, Roberto Furfaro

We use reinforcement meta learning to optimize a line of sight curvature policy that increases the effectiveness of a guidance system against maneuvering targets. The policy is imp…

cs.CL2022

Extracting Space Situational Awareness Events from News Text

Zhengnan Xie, Alice Saebom Kwak, Enfa George +5

Space situational awareness typically makes use of physical measurements from radar, telescopes, and other assets to monitor satellites and other spacecraft for operational, naviga…

eess.SY2021

Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning

Brian Gaudet, Roberto Furfaro

An adaptive guidance system suitable for the terminal phase trajectory of a hypersonic strike weapon is optimized using reinforcement meta learning. The guidance system maps observ…

cs.RO2021

Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning

Brian Gaudet, Kris Drozd, Ryan Meltzer +1

We use Reinforcement Meta Learning to optimize an adaptive guidance system suitable for the approach phase of a gliding hypersonic vehicle. Adaptability is achieved by optimizing o…

astro-ph.EP202122 cited

The Effect of Inefficient Accretion on Planetary Differentiation

Saverio Cambioni, Seth A. Jacobson, Alexandre Emsenhuber +5

Pairwise collisions between terrestrial embryos are the dominant means of accretion during the last stage of planet formation. Hence, their realistic treatment in N-body studies is…

physics.comp-ph20209 cited

Physics-Informed Extreme Theory of Functional Connections Applied to Data-Driven Parameters Discovery of Epidemiological Compartmental Models

Enrico Schiassi, Andrea D'Ambrosio, Mario De Florio +2

In this work we apply a novel, accurate, fast, and robust physics-informed neural network framework for data-driven parameters discovery of problems modeled via parametric ordinary…