Deep Learning In Introductory Physics
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Deep Learning In Introductory Physics
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Author : Mark J. Lattery
language : en
Publisher: IAP
Release Date : 2016-10-01
Deep Learning In Introductory Physics written by Mark J. Lattery and has been published by IAP this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-01 with Education categories.
Deep Learning in Introductory Physics: Exploratory Studies of Model?Based Reasoning is concerned with the broad question of how students learn physics in a model?centered classroom. The diverse, creative, and sometimes unexpected ways students construct models, and deal with intellectual conflict, provide valuable insights into student learning and cast a new vision for physics teaching. This book is the first publication in several years to thoroughly address the “coherence versus fragmentation” debate in science education, and the first to advance and explore the hypothesis that deep science learning is regressive and revolutionary. Deep Learning in Introductory Physics also contributes to a growing literature on the use of history and philosophy of science to confront difficult theoretical and practical issues in science teaching, and addresses current international concern over the state of science education and appropriate standards for science teaching and learning. The book is divided into three parts. Part I introduces the framework, agenda, and educational context of the book. An initial study of student modeling raises a number of questions about the nature and goals of physics education. Part II presents the results of four exploratory case studies. These studies reproduce the results of Part I with a more diverse sample of students; under new conditions (a public debate, peer discussions, and group interviews); and with new research prompts (model?building software, bridging tasks, and elicitation strategies). Part III significantly advances the emergent themes of Parts I and II through historical analysis and a review of physics education research. ENDORSEMENTS: "In Deep Learning in Introductory Physics, Lattery describes his extremely innovative course in which students' ideas about motion are elicited, evaluated with peers, and revised through experiment and discussion. The reader can see the students' deep engagement in constructive scientific modeling, while students deal with counter-intuitive ideas about motion that challenged Galileo in many of the same ways. Lattery captures students engaging in scientific thinking skills, and building difficult conceptual understandings at the same time. This is the 'double outcome' that many science educators have been searching for. The case studies provide inspiring examples of innovative course design, student sensemaking and reasoning, and deep conceptual change." ~ John Clement, University of Massachusetts—Amherst, Scientific Reasoning Research Institute "Deep Learning in Introductory Physics is an extraordinary book and an important intellectual achievement in many senses. It offers new perspectives on science education that will be of interest to practitioners, to education researchers, as well as to philosophers and historians of science. Lattery combines insights into model-based thinking with instructive examples from the history of science, such as Galileo’s struggles with understanding accelerated motion, to introduce new ways of teaching science. The book is based on first-hand experiences with innovative teaching methods, reporting student’s ideas and discussions about motion as an illustration of how modeling and model-building can help understanding science. Its lively descriptions of these experiences and its concise presentations of insights backed by a rich literature on education, cognitive science, and the history and philosophy of science make it a great read for everybody interested in how models shape thinking processes." ~ Dr. Jürgen Renn, Director, Max Planck Institute for the History of Science
Deep Learning For Fluid Simulation And Animation
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Author : Gilson Antonio Giraldi
language : en
Publisher: Springer Nature
Release Date : 2023-11-24
Deep Learning For Fluid Simulation And Animation written by Gilson Antonio Giraldi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-24 with Mathematics categories.
This book is an introduction to the use of machine learning and data-driven approaches in fluid simulation and animation, as an alternative to traditional modeling techniques based on partial differential equations and numerical methods – and at a lower computational cost. This work starts with a brief review of computability theory, aimed to convince the reader – more specifically, researchers of more traditional areas of mathematical modeling – about the power of neural computing in fluid animations. In these initial chapters, fluid modeling through Navier-Stokes equations and numerical methods are also discussed. The following chapters explore the advantages of the neural networks approach and show the building blocks of neural networks for fluid simulation. They cover aspects related to training data, data augmentation, and testing. The volume completes with two case studies, one involving Lagrangian simulation of fluids using convolutional neural networks and the other using Generative Adversarial Networks (GANs) approaches.
Introduction To Deep Learning For Engineers
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Author : Tariq M. Arif
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Introduction To Deep Learning For Engineers written by Tariq M. Arif and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model. In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case. The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
Mathematical Aspects Of Deep Learning
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Author : Philipp Grohs
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-22
Mathematical Aspects Of Deep Learning written by Philipp Grohs and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-22 with Computers categories.
In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.
Deep Learning In Computational Mechanics
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Author : Stefan Kollmannsberger
language : en
Publisher: Springer Nature
Release Date : 2021-08-05
Deep Learning In Computational Mechanics written by Stefan Kollmannsberger and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-05 with Technology & Engineering categories.
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning’s fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book’s main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature’s evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python.
Applying Machine Learning In Science Education Research
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Author : Peter Wulff
language : en
Publisher: Springer Nature
Release Date : 2025-02-28
Applying Machine Learning In Science Education Research written by Peter Wulff and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-28 with Science categories.
This open access textbook offers science education researchers a hands-on guide for learning, critically examining, and integrating machine learning (ML) methods into their science education research projects. These methods power many artificial intelligence (AI)-based technologies and are widely adopted in science education research. ML can expand the methodological toolkit of science education researchers and provide novel opportunities to gain insights on science-related learning and teaching processes, however, applying ML poses novel challenges and is not suitable for every research context. The volume first introduces the theoretical underpinnings of ML methods and their connections to methodological commitments in science education research. It then presents exemplar case studies of ML uses in both formal and informal science education settings. These case studies include open-source data, executable programming code, and explanations of the methodological criteria and commitments guiding ML use in each case. The textbook concludes with a discussion of opportunities and potential future directions for ML in science education. This textbook is a valuable resource for science education lecturers, researchers, under-graduate, graduate and postgraduate students seeking new ways to apply ML in their work.
Successful Science And Engineering Teaching
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Author : Calvin S. Kalman
language : en
Publisher: Springer
Release Date : 2017-10-11
Successful Science And Engineering Teaching written by Calvin S. Kalman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-11 with Science categories.
The intent of this book is to describe how a professor can provide a learning environment that assists students in coming to grips with the nature of science and engineering, to understand science and engineering concepts, and to solve problems in science and engineering courses. The book is based upon articles published in Science Educational Research and which are grounded in educational research (both quantitative and qualitative) performed by the author over many years.
Successful Science And Engineering Teaching In Colleges And Universities 2nd Edition
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Author : Calvin S. Kalman
language : en
Publisher: IAP
Release Date : 2017-06-01
Successful Science And Engineering Teaching In Colleges And Universities 2nd Edition written by Calvin S. Kalman and has been published by IAP this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-01 with Education categories.
Based on the author's work in science and engineering educational research, this book offers broad, practical strategies for teaching science and engineering courses and describes how faculty can provide a learning environment that helps students comprehend the nature of science, understand science concepts, and solve problems in science courses. This book's student?centered approach focuses on two main themes: writing to learn (especially Reflective Writing) and interactive activities (collaborative groups and labatorials). When faculty incorporate these methods into their courses, students gain a better understanding of science as a connected structure of concepts rather than as a toolkit of assorted practices.
Dynamic Data Driven Applications Systems
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Author : Erik Blasch
language : en
Publisher: Springer Nature
Release Date : 2024-02-26
Dynamic Data Driven Applications Systems written by Erik Blasch and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-26 with Computers categories.
This book constitutes the refereed proceedings of the 4th International Conference on Dynamic Data Driven Applications Systems, DDDAS 2022, which took place in Cambridge, MA, USA, during October 6–10, 2022. The 31 regular papers in the main track and 5 regular papers from the Wildfires panel, as well as one workshop paper, were carefully reviewed and selected for inclusion in the book. They were organized in following topical sections: DDAS2022 Main-Track Plenary Presentations; Keynotes; DDDAS2022 Main-Track: Wildfires Panel; Workshop on Climate, Life, Earth, Planets.
Data Science In Engineering Vol 10
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Author : Thomas Matarazzo
language : en
Publisher: CRC Press
Release Date : 2025-08-07
Data Science In Engineering Vol 10 written by Thomas Matarazzo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-07 with Computers categories.
Data Science in Engineering, Volume 10: Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics, 2024, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection Data-driven System Prognostics.