Titlebook: Machine Learning in Modeling and Simulation; Methods and Applicat Timon Rabczuk,Klaus-Jürgen Bathe Book 2023 The Editor(s) (if applicable)

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Reduced Order Modeling,ection with machine learning techniques. Although the presentation is applicable to many problems in science and engineering, the focus is first-order evolution problems in time and, more specifically, flow problems. Particular emphasis is put on the distinction between intrusive models, which make

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Regression Models for Machine Learning,pectives. The non-Bayesian regression models, including the least square regression, ridge regression, and support vector regression, equipped or not equipped with kernel trick, are first examined as they share the same principle, which is to find an element in the parametrically indexed hypothesis

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Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling,ation of diverse physical properties. MLIPs moreover offer extraordinary capabilities to conduct first-principles multiscale modeling, enabling the modeling of nanostructured materials at continuum level, with quantum mechanics level of accuracy and affordable computational costs. In this chapter, w

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Regression Models for Machine Learning,p a unique learning skill, i.e. active learning, which aims at devising optimal design strategies for minimizing the number of simulator calls, especially when each call is computationally cumbersome. This is shown to be effective when applied to cutting-edge research on Bayesian numerical analysis

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Book 2023 and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering..