Titlebook: Mechanistic Data Science for STEM Education and Applications; Wing Kam Liu,Zhengtao Gan,Mark Fleming Textbook 2021 The Editor(s) (if appli

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https://doi.org/10.1007/978-3-030-87832-0Data science; machine learning; deep learning; mechanistic modeling; mathematical science and engineerin

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Introduction to Mechanistic Data Science,Data science and machine learning are pushing the limits of the hardware and computer algorithms more and more. Additionally, mathematical science and engineering is constantly taxed with challenging the current status quo. In this perspective, it is paramount to explore the possibility to perform t

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Multimodal Data Generation and Collection, This process is a key part of the scientific process and generally involves observation and careful recording. Costly data collection from physical observation can be enhanced by taking advantage of the modern computer hardware and software to simulate the physical experiments and generate further

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Optimization and Regression, and engineering, linear regression is fundamental to understanding more advanced regression methods. In particular, gradient descent will be discussed as a technique with a wide range of applications. Key to understanding linear regression are concepts of optimization. In this chapter, the fundamen

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Extraction of Mechanistic Features,red, disorganized and jumbled formats. It is necessary process and re-organize data to make it possible to achieve useful outcomes using data science. In addition, it can be important to understand the scientific principles associated with data to enhance the feature extraction process. This chapter

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Deep Learning for Regression and Classification,eural networks (ANN)) (Schmidhuber, Neural Networks, 61, 85–117, 2015). An ANN is a computer system inspired by the biological neural networks in the human brain (Chen et al., Sensors, 19, 2047, 2019). The term “deep” refers to the use of multiple layers of neurons (three or more) in the ANN. Deep l