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Author: Daniela Steffes-lai Publisher: Logos Verlag Berlin GmbH ISBN: 3832536965 Category : Mathematics Languages : en Pages : 232
Book Description
This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry.
Author: Daniela Steffes-lai Publisher: Logos Verlag Berlin GmbH ISBN: 3832536965 Category : Mathematics Languages : en Pages : 232
Book Description
This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry.
Author: Stefan Peter Müller Publisher: Logos Verlag Berlin GmbH ISBN: 3832554440 Category : Mathematics Languages : en Pages : 232
Book Description
Big data thrives on extracting knowledge from a large number of data sets. But how is an application possible when a single data set is several gigabytes in size? The innovative data compression techniques from the field of machine learning and modeling using Bayesian networks, which have been theoretically developed and practically implemented here, can reduce these huge amounts of data to a manageable size. By eliminating redundancies in location, time, and between simulation results, data reductions to less than 1% of the original size are possible. The developed method represents a promising approach whose use goes far beyond the application example of crash test simulations chosen here.
Author: Ben Adcock Publisher: Society for Industrial and Applied Mathematics (SIAM) ISBN: 9781611976878 Category : Approximation theory Languages : en Pages : 0
Book Description
"This is a book about polynomial approximation in high dimensions"--
Author: Martin J. Wainwright Publisher: Cambridge University Press ISBN: 1108498027 Category : Business & Economics Languages : en Pages : 571
Book Description
A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.
Author: Hala Nelson Publisher: "O'Reilly Media, Inc." ISBN: 1098107608 Category : Computers Languages : en Pages : 605
Book Description
Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions
Author: Igor Farkaš Publisher: Springer Nature ISBN: 3030616096 Category : Computers Languages : en Pages : 891
Book Description
The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.
Author: Puneet Singla Publisher: CRC Press ISBN: 1584887702 Category : Mathematics Languages : en Pages : 316
Book Description
Unifying the most important methodology in this field, Multi-Resolution Methods for Modeling and Control of Dynamical Systems explores existing approximation methods as well as develops new ones for the approximate solution of large-scale dynamical system problems. It brings together a wide set of material from classical orthogonal function
Author: Radim Bris Publisher: CRC Press ISBN: 1315641658 Category : Mathematics Languages : en Pages : 352
Book Description
Applied Mathematics in Engineering and Reliability contains papers presented at the International Conference on Applied Mathematics in Engineering and Reliability (ICAMER 2016, Ho Chi Minh City, Viet Nam, 4-6 May 2016). The book covers a wide range of topics within mathematics applied in reliability, risk and engineering, including:- Risk and Relia
Author: Olivier Roux Publisher: Springer ISBN: 3319234013 Category : Computers Languages : en Pages : 302
Book Description
This book constitutes the refereed proceedings of the 13th International Conference on Computational Methods in Systems Biology, CMSB 2015, held in Nantes, France, in September 2015. The 20 full papers and 2 short papers presented were carefully reviewed and selected from 43 full and 4 short paper submissions. The papers cover a wide range of topics in the analysis of biological systems, networks and data such as model checking, stochastic analysis, hybrid systems, circadian clock, time series data, logic programming, and constraints solving ranging from intercellular to multiscale.
Author: Daniela Steffes-lai
Author: Daniela Steffes-lai
Author: Stefan Peter Müller
Author: Ben Adcock
Author: Martin J. Wainwright
Author: Hala Nelson
Author: Igor Farkaš
Author: Puneet Singla
Author: Roman Vershynin
Author: Radim Bris
Author: Olivier Roux