Journal ArticleDOI
Aerodynamic shape optimization using a novel optimizer based on machine learning techniques
TLDR
A new optimizer is proposed and tested for a typical aerodynamic shape optimization of missile control surfaces with computational fluid dynamics (CFD), which significantly decreases the required CFD calls by over 62.5%.About:
This article is published in Aerospace Science and Technology.The article was published on 2019-03-01. It has received 103 citations till now. The article focuses on the topics: Artificial neural network & Reinforcement learning.read more
Citations
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Journal ArticleDOI
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
TL;DR: In this article, the authors used deep reinforcement learning (DRL) to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain.
Journal ArticleDOI
Reinforcement learning in dual-arm trajectory planning for a free-floating space robot
TL;DR: A model-free reinforcement learning strategy is proposed for training a policy for online trajectory planning without establishing the dynamic and kinematic models of the space robot.
Posted Content
A review on Deep Reinforcement Learning for Fluid Mechanics
TL;DR: Understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods is provided.
Journal ArticleDOI
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning.
TL;DR: It is shown that the DRL controller is able to significantly reduce the lift and drag fluctuations and to actively reduce the drag by approximately 5.7%, at $Re$=100, 200, 300, and 400 respectively.
Journal ArticleDOI
Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization
TL;DR: In this article, a short review of the state of the art of the use of DRL within fluid mechanics, focusing on control and optimal design problems is provided, with an insight into the current state-of-the-art.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
Martín Abadi,Paul Barham,Jianmin Chen,Zhifeng Chen,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Geoffrey Irving,Michael Isard,Manjunath Kudlur,Josh Levenberg,Rajat Monga,Sherry Moore,Derek G. Murray,Benoit Steiner,Paul A. Tucker,Vijay K. Vasudevan,Pete Warden,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +21 more
TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.
Posted Content
TensorFlow: A system for large-scale machine learning
Martín Abadi,Paul Barham,Jianmin Chen,Zhifeng Chen,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Geoffrey Irving,Michael Isard,Manjunath Kudlur,Josh Levenberg,Rajat Monga,Sherry Moore,Derek G. Murray,Benoit Steiner,Paul A. Tucker,Vijay K. Vasudevan,Pete Warden,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +21 more
TL;DR: The TensorFlow dataflow model is described and the compelling performance that Tensor Flow achieves for several real-world applications is demonstrated.
Posted Content
Continuous control with deep reinforcement learning
Timothy P. Lillicrap,Jonathan J. Hunt,Alexander Pritzel,Nicolas Heess,Tom Erez,Yuval Tassa,David Silver,Daan Wierstra +7 more
TL;DR: This work presents an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces, and demonstrates that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.