activity
20182023
most citedReinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets

145 citations · 814 across the 26 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

physics.comp-ph2020★ 9 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…

cs.LG2020★ 11 cited

Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations

Enrico Schiassi, Carl Leake, Mario De Florio +3

In this work we present a novel, accurate, and robust physics-informed method for solving problems involving parametric differential equations (DEs) called the Extreme Theory of Fu…

eess.SY2020★ 30 cited

Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop

Brian Gaudet, Roberto Furfaro, Richard Linares +1

We use Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target. The…

eess.SY2020★ 109 cited

Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach

Roberto Furfaro, Andrea Scorsoglio, Richard Linares +1

Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-E…

math.OC2020★ 43 cited

Fuel-Efficient Powered Descent Guidance on Large Planetary Bodies via Theory of Functional Connections

Hunter Johnston, Enrico Schiassi, Roberto Furfaro +1

In this paper we present a new approach to solve the fuel-efficient powered descent guidance problem on large planetary bodies with no atmosphere (e.g. the Moon or Mars) using the…

astro-ph.EP2020★ 40 cited

Realistic On-the-fly Outcomes of Planetary Collisions II: Bringing Machine Learning to N-body Simulations

Alexandre Emsenhuber, Saverio Cambioni, Erik Asphaug +3

Terrestrial planet formation theory is at a bottleneck, with the growing realization that pairwise collisions are treated far too simply. Here, and in our companion paper (Cambioni…