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Opening the black box of deep learning
The great success of deep learning shows that its technology contains pr...
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How the fundamental concepts of mathematics and physics explain deep learning
Starting from the Fermat's principle of least action, which governs clas...
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A Tutorial on Deep Learning for Music Information Retrieval
Following their success in Computer Vision and other areas, deep learnin...
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Deep learning in agriculture: A survey
Deep learning constitutes a recent, modern technique for image processin...
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Database Meets Deep Learning: Challenges and Opportunities
Deep learning has recently become very popular on account of its incredi...
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Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
We present hidden fluid mechanics (HFM), a physics informed deep learnin...
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The Newton Scheme for Deep Learning
We introduce a neural network (NN) strictly governed by Newton's Law, wi...
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Deep Learning for Classical Mechanics
Deep learning has been widely and actively used in various research areas. Recently, in the subject so-called gauge/gravity duality, a new deep learning technique which deals with classical equations of motion has been proposed. This method is a little different from standard deep learning techniques in the sense that not only do we have the right final answers but also obtain physical understanding of learning parameters. Building on this idea, we apply the deep learning technique to simple classical mechanics problems. The type of problems we address is how to find the unknown force, by the deep learning technique, only from the initial and final data sets. We demonstrate that our deep learning technique is successful for simple cases: one dimensional velocity or position-dependent force. In our opinion, this method has a big potential for wider applications to physics and computer science both in education and research.
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