Browsing by Author "Demirezen, Umut M."

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    Development of UCAV fleet autonomy by reinforcement learning in a wargame simulation environment
    (AIAA, 2021-01-04) Yuksek, Burak; Demirezen, Umut M.; Inalhan, Gokhan
    In this study, we develop a machine learning based fleet autonomy for Unmanned Combat Aerial Vehicles (UCAVs) utilizing a synthetic simulation-based wargame environment. Aircraft survivability is modeled as Markov processes. Mission success metrics are developed to introduce collision avoidance and survival probability of the fleet. Flight path planning is performed utilizing the proximal policy optimization (PPO) based reinforcement learning method to obtain attack patterns with a multi-objective mission success criteria corresponding to the mission success metrics. Performance of the proposed system is evaluated by utilizing the Monte Carlo analysis in which a wider initial position interval is used when compared to the defined interval in the training phase. This provides a preliminary insight about the generalization ability of the RL agent
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    A novel physics informed deep learning method for simulation-based modelling
    (AIAA, 2021-01-04) Karali, Hasan; Demirezen, Umut M.; Yukselen, Mahmut A.; Inalhan, Gokhan
    In this paper, we present a brief review of the state of the art physics informed deep learning methodology and examine its applicability, limits, advantages, and disadvantages via several applications. The main advantage of this method is that it can predict the solution of the partial differential equations by using only boundary and initial conditions without the need for any training data or pre-process phase. Using physics informed neural network algorithms, it is possible to solve partial differential equations in many different problems encountered in engineering studies with a low cost and time instead of traditional numerical methodologies. A direct comparison between the initial results of the current model, analytical solutions, and computational fluid dynamics methods shows very good agreement. The proposed methodology provides a crucial basis for solution of more advance partial differential equation systems and offers a new analysis and mathematical modelling tool for aerospace applications