Computational design exploration of rocket nozzle using deep reinforcement learning

Computational design exploration of rocket nozzle using deep reinforcement learning

Deep Reinforcement Learning (DRL) has emerged as a powerful tool for solving high-dimensional optimization problems in complex, unexplored domains. This study presents a novel application of DRL for computational design exploration of rocket nozzles, demonstrating its superiority over traditional pa...

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Bibliographic Details
Main Authors: Aagashram Neelakandan, Arockia Selvakumar Arockia Doss, Natrayan Lakshmaiya
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:Results in Engineering
Subjects:
Design exploration
Reinforcement learning
Computational fluid dynamics
Nozzle
Online Access:http://www.sciencedirect.com/science/article/pii/S2590123025005171
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