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Simão Marques, Lucas Kob, Trevor T. Robinson and Weigang Yao
This work presents a strategy to build reduced-order models suitable for aerodynamic shape optimisation, resulting in a multifidelity optimisation framework. A reduced-order model (ROM) based on a discrete empirical interpolation (DEIM) method is employe...
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Mahsa Yousefi and Ángeles Martínez
While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi...
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Warren Hare and Gabriel Jarry-Bolduc
This paper examines a calculus-based approach to building model functions in a derivative-free algorithm. This calculus-based approach can be used when the objective function considered is defined via more than one blackbox. Two versions of a derivative-...
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Minseok Kong and Jungmin So
There are several automated stock trading programs using reinforcement learning, one of which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL agents and make an ensemble with three different actor?critic algorithms: Advant...
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Yanbo Fu, Wenjie Zhao and Liu Liu
Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications. This study focuses on developing a safe reinforcement learning method for back-transition control be...
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Dong Sui, Chenyu Ma and Chunjie Wei
To assist air traffic controllers (ATCOs) in resolving tactical conflicts, this paper proposes a conflict detection and resolution mechanism for handling continuous traffic flow by adopting finite discrete actions to resolve conflicts. The tactical confl...
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Jun Huang and Yidong Zeng
This paper presents a fast trajectory optimization method combining the hp-Legendre pseudospectral method and convex optimization for the 6-Degree-of-Freedom rocket-powered landing problem. To accelerate calculations, this paper combines the Legendre pse...
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Joseph Isabona, Agbotiname Lucky Imoize, Oluwasayo Akinloye Akinwumi, Okiemute Roberts Omasheye, Emughedi Oghu, Cheng-Chi Lee and Chun-Ta Li
Benchmarking different optimization algorithms is tasky, particularly for network-based cellular communication systems. The design and management process of these systems involves many stochastic variables and complex design parameters that demand an unb...
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S. Indrapriyadarsini, Shahrzad Mahboubi, Hiroshi Ninomiya, Takeshi Kamio and Hideki Asai
Gradient-based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving hi...
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Jijun Tong, Shuai Xu, Fangliang Wang and Pengjia Qi
This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg?Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter a...
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