Titlebook: Advances in Real-Time and Autonomous Systems; Proceedings of the 1 Herwig Unger,Marcel Schaible Conference proceedings 2024 The Editor(s) (

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Aparajita Datta,Abhishek Dey,Kashi Nath Dey. A case study focusing on road traffic data is expected to demonstrate the effectiveness of this concept, utilizing real-world traffic data and encoding basic traffic flow equations with PINNs. The anticipated results suggest that the ensemble of PINNs with transfer learning will surpass traditiona

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https://doi.org/10.1007/978-981-13-8578-0ment. In essence, this paper serves as a comprehensive guide, steering readers through the evolution, intricacies, and future directions of Language-Driven Image Generation Models, fostering a deeper understanding and encouraging continued exploration in this dynamic interdisciplinary field.

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,Algorithmic Foundations of Reinforcement Learning,v Decision Processes (MDPs) and dynamic programming are covered, describing the principles and techniques for addressing model-based problems within MDP frameworks. The most significant model-free reinforcement learning algorithms, including Q-learning and actor-critic methods are explained in detai

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,Autonomous Emergency Landing of an Aircraft in Case of Total Engine-Out,ht route in gliding must be made as soon as possible. It is important to divide the remaining height available when deciding on an emergency landing in such a way that the runway threshold is still reached at a suitable flaring height despite the influence of wind. This route planning can be carried

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,Ensemble Learning with Physics-Informed Neural Networks for Harsh Time Series Analysis,hasticity are formidable. This paper introduces a novel approach that synergizes Physics-Informed Neural Networks (PINNs) and Ensemble Transfer Learning (ETL) to address these challenges, enhancing the accuracy and reliability of time series analysis and prediction. PINNs, by incorporating domain kn

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,Language Meets Vision: A Critical Survey on Cutting-Edge Prompt-Based Image Generation Models,age-Driven Image Generation Models. This comprehensive paper navigates the intricate realm of language-driven image generation models. Beginning with a comprehensive specification book, this paper offers guidelines for practitioners and researchers in the domain of prompt-based generative models. A

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