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| Author | : Michel Bergmann |
| Publisher | : Frontiers Media SA |
| Total Pages | : 178 |
| Release | : 2023-01-05 |
| Genre | : Science |
| ISBN | : 2832510701 |
| Author | : Miguel A. Mendez |
| Publisher | : Cambridge University Press |
| Total Pages | : 470 |
| Release | : 2022-12-31 |
| Genre | : Science |
| ISBN | : 110890226X |
Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Fluid mechanics is historically a big data field and offers a fertile ground for developing and applying data-driven methods, while also providing valuable shortcuts, constraints, and interpretations based on its powerful connections to basic physics. Thus, hybrid approaches that leverage both methods based on data as well as fundamental principles are the focus of active and exciting research. Originating from a one-week lecture series course by the von Karman Institute for Fluid Dynamics, this book presents an overview and a pedagogical treatment of some of the data-driven and machine learning tools that are leading research advancements in model-order reduction, system identification, flow control, and data-driven turbulence closures.
| Author | : Steven L. Brunton |
| Publisher | : Cambridge University Press |
| Total Pages | : 615 |
| Release | : 2022-05-05 |
| Genre | : Computers |
| ISBN | : 1009098489 |
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
| Author | : Steven L. Brunton |
| Publisher | : Cambridge University Press |
| Total Pages | : 616 |
| Release | : 2022-05-05 |
| Genre | : Computers |
| ISBN | : 1009115634 |
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
| Author | : U.S. National Committee on Theoretical and Applied Mechanics |
| Publisher | : American Institute of Physics |
| Total Pages | : 362 |
| Release | : 1996-03-22 |
| Genre | : Science |
| ISBN | : |
Market: Those interested in fluid dynamics and the related fields of oceanography, meteorology, and mechanical, aerospace, chemical, and civil engineering. This monograph is a report of a meeting sponsored by the National Science Foundation to determine research trends and consequent funding/research needs in fluid dynamics. The book covers major industries, technologies, and environmental issues affected by fluid mechanics, as well as the direction future research in the field should take. The areas covered not only fill important gaps in the literature, they are crucial to the resolution of serious global and regional environmental problems. In addition, the book emphasizes the impact of the research areas on commercial questions and on issues affecting public policy.
| Author | : J. Nathan Kutz |
| Publisher | : SIAM |
| Total Pages | : 241 |
| Release | : 2016-11-23 |
| Genre | : Science |
| ISBN | : 1611974496 |
Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
| Author | : J. Nathan Kutz |
| Publisher | : Oxford University Press |
| Total Pages | : 657 |
| Release | : 2013-08-08 |
| Genre | : Computers |
| ISBN | : 0199660336 |
Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.
| Author | : Carlo Novara |
| Publisher | : Control, Robotics and Sensors |
| Total Pages | : 300 |
| Release | : 2019-09 |
| Genre | : Technology & Engineering |
| ISBN | : 1785617125 |
Using important examples, this book showcases the potential of the latest data-based and data-driven methodologies for filter and control design. It discusses the most important classes of dynamic systems, along with the statistical and set membership analysis and design frameworks.
| Author | : |
| Publisher | : |
| Total Pages | : 104 |
| Release | : 1776 |
| Genre | : |
| ISBN | : |
| Author | : National Academies of Sciences, Engineering, and Medicine |
| Publisher | : National Academies Press |
| Total Pages | : 79 |
| Release | : 2019-11-09 |
| Genre | : Technology & Engineering |
| ISBN | : 0309494206 |
Additive manufacturing (AM) is the process in which a three-dimensional object is built by adding subsequent layers of materials. AM enables novel material compositions and shapes, often without the need for specialized tooling. This technology has the potential to revolutionize how mechanical parts are created, tested, and certified. However, successful real-time AM design requires the integration of complex systems and often necessitates expertise across domains. Simulation-based design approaches, such as those applied in engineering product design and material design, have the potential to improve AM predictive modeling capabilities, particularly when combined with existing knowledge of the underlying mechanics. These predictive models have the potential to reduce the cost of and time for concept-to-final-product development and can be used to supplement experimental tests. The National Academies convened a workshop on October 24-26, 2018 to discuss the frontiers of mechanistic data-driven modeling for AM of metals. Topics of discussion included measuring and modeling process monitoring and control, developing models to represent microstructure evolution, alloy design, and part suitability, modeling phases of process and machine design, and accelerating product and process qualification and certification. These topics then led to the assessment of short-, immediate-, and long-term challenges in AM. This publication summarizes the presentations and discussions from the workshop.
