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Author: Roland Ewald
Publisher: Springer Science & Business Media
ISBN: 3834881511
Category : Computers
Languages : en
Pages : 387
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Book Description
To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments without demanding expert knowledge on simulation. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and describes its integration into the open-source modelling and simulation framework James II. Its selection mechanisms are able to cope with three situations: no prior knowledge is available, the impact of problem features on simulator performance is unknown, and a relationship between problem features and algorithm performance can be established empirically. The author concludes with an experimental evaluation of the developed methods.
Author: Roland Ewald
Publisher: Springer Science & Business Media
ISBN: 3834881511
Category : Computers
Languages : en
Pages : 387
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Book Description
To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments without demanding expert knowledge on simulation. Roland Ewald analyzes and discusses existing approaches to solve the algorithm selection problem in the context of simulation. He introduces a framework for automatic simulation algorithm selection and describes its integration into the open-source modelling and simulation framework James II. Its selection mechanisms are able to cope with three situations: no prior knowledge is available, the impact of problem features on simulator performance is unknown, and a relationship between problem features and algorithm performance can be established empirically. The author concludes with an experimental evaluation of the developed methods.
Author: Matúš Maciak
Publisher: Springer Nature
ISBN: 3030488144
Category : Mathematics
Languages : en
Pages : 159
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Book Description
This book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019. Real-life problems demand statistical solutions, which in turn require new and profound mathematical methods. As such, the book is not only a collection of solved problems but also a source of new methods and their practical extensions. The authoritative contributions focus on analytical methods in statistics, asymptotics, estimation and Fisher information, robustness, stochastic models and inequalities, and other related fields; further, they address e.g. average autoregression quantiles, neural networks, weighted empirical minimum distance estimators, implied volatility surface estimation, the Grenander estimator, non-Gaussian component analysis, meta learning, and high-dimensional errors-in-variables models.
Author: Andelfinger, Philipp Josef
Publisher: KIT Scientific Publishing
ISBN: 3731505118
Category : Electronic computers. Computer science
Languages : en
Pages : 166
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Book Description
Although computer networks are inherently parallel systems, the parallel execution of network simulations on interconnected processors frequently yields only limited benefits. In this thesis, methods are proposed to estimate and understand the parallelization potential of network simulations. Further, mechanisms and architectures for exploiting the massively parallel processing resources of modern graphics cards to accelerate network simulations are proposed and evaluated.
Author: Fabian Lorig
Publisher: Springer
ISBN: 365827588X
Category : Computers
Languages : en
Pages : 394
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Book Description
Fabian Lorig develops a procedure model for hypothesis-driven simulation studies which supports the design, conducting, and analysis of simulation experiments. It is aimed at facilitating the execution of simulation studies with regard to the replicability and reproducibility of the results. In comparison to existing models, this approach is based on a formally specified hypothesis. Each step of the simulation study can be adapted to the central hypothesis and performed in such a way that it can optimally contribute to the verification and thus to the confirmation or rejection of the hypothesis.
Author: George Corliss
Publisher: Springer Science & Business Media
ISBN: 1461300754
Category : Computers
Languages : en
Pages : 431
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Book Description
A survey book focusing on the key relationships and synergies between automatic differentiation (AD) tools and other software tools, such as compilers and parallelizers, as well as their applications. The key objective is to survey the field and present the recent developments. In doing so the topics covered shed light on a variety of perspectives. They reflect the mathematical aspects, such as the differentiation of iterative processes, and the analysis of nonsmooth code. They cover the scientific programming aspects, such as the use of adjoints in optimization and the propagation of rounding errors. They also cover "implementation" problems.
Author: H.R. Madala
Publisher: CRC Press
ISBN: 1351081942
Category : Computers
Languages : en
Pages : 381
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Book Description
Discusses algorithm development, structure, and behavior Presents comprehensive coverage of algorithms useful for complex systems modeling Includes recent studies on clusterization and recognition problems Provides listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs
Author: H.R. Madala
Publisher: CRC Press
ISBN: 1351090399
Category : Computers
Languages : en
Pages : 353
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Book Description
Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behavior; comprehensive coverage of all types of algorithms useful for this subject; and applications of various modeling activities (e.g., environmental systems, noise immunity, economic systems, clusterization, and neural networks). It presents recent studies on clusterization and recognition problems, and it includes listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs. Inductive Learning Algorithms for Complex Systems Modeling will be a valuable reference for graduate students, research workers, and scientists in applied mathematics, statistics, computer science, and systems science disciplines. The book will also benefit engineers and scientists from applied fields such as environmental studies, oceanographic modeling, weather forecasting, air and water pollution studies, economics, hydrology, agriculture, fisheries, and time series evaluations.
Author: Thomas Bartz-Beielstein
Publisher: Springer
ISBN: 3030187640
Category : Technology & Engineering
Languages : en
Pages : 291
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Book Description
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223
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Book Description
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Author: William Fitzgibbon
Publisher: Springer
ISBN: 940179054X
Category : Technology & Engineering
Languages : en
Pages : 252
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Book Description
This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problems with multiple criteria; four articles are on numerical methods for applied problems modeled with nonlinear PDEs; two contributions are on abstract estimates for error analysis; finally one paper deals with rare events in the context of uncertainty quantification. Applications include aerospace, glaciology and nonlinear elasticity. Herein is a selection of contributions from speakers at two conferences on applied mathematics held in June 2012 at the University of Jyväskylä, Finland. The first conference, “Optimization and PDEs with Industrial Applications” celebrated the seventieth birthday of Professor Jacques Périaux of the University of Jyväskylä and Polytechnic University of Catalonia (Barcelona Tech) and the second conference, “Optimization and PDEs with Applications” celebrated the seventy-fifth birthday of Professor Roland Glowinski of the University of Houston. This work should be of interest to researchers and practitioners as well as advanced students or engineers in computational and applied mathematics or mechanics.
