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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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will be combined to generate a numerical tool to predict rupture of materials under blast loading. Machine Learning techniques will also be investigated throughout the project to rapidly assess the large
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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with other sensing approaches, such as from airborne, satellite platforms and other on-site data sources. 4. Develop edge-based machine-learning techniques to provide near-instantaneous emission