Books on the topic 'Simulation of proof'

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1

Roever, W. P. de. Data refinement: Model-oriented proof methods and their comparison. Cambridge: Cambridge University Press, 2009.

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2

1966-, Engelhardt Kai, and Buth Karl-Heinz, eds. Data refinement: Model-oriented proof methods and their comparison. Cambridge, UK: Cambridge University Press, 1998.

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3

Mermet, Jean, ed. VHDL for Simulation, Synthesis and Formal Proofs of Hardware. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3562-1.

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4

Mermet, Jean. VHDL for simulation, synthesis and formal proofs of hardware. Dordrecht: Springer Science+Business Media, B.V., 1992.

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5

Veanes, Margus. Tests and Proofs: 7th International Conference, TAP 2013, Budapest, Hungary, June 16-20, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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6

Jacques, Julliand, and SpringerLink (Online service), eds. Tests and Proofs: 6th International Conference, TAP 2012, Prague, Czech Republic, May 31 – June 1, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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7

Yungk, George Lee. The development of an intelligent graphics interface for the RESA wargaming simulation terminals: A Proof of Concept. 1988.

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8

Roever, Willem-Paul de, and Kai Engelhardt. Data Refinement: Model-Oriented Proof Methods and their Comparison (Cambridge Tracts in Theoretical Computer Science). Cambridge University Press, 2001.

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9

Dawid, Herbert, Simon Gemkow, Philipp Harting, Sander van der Hoog, and Michael Neugart. Agent-Based Macroeconomic Modeling and Policy Analysis. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.19.

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This chapter introduces the Eurace@Unibi model, one of the agent-based simulation models that are relatively new additions to the toolbox of macroeconomists, and the research that has been done within this framework. It shows how an agent-based model can be used to identify economic mechanisms and how it can be applied to spatial policy analysis. The assessment is that agent-based models in economics have passed the proof-of-concept phase and it is now time to move beyond that stage. It has been shown that new kinds of insights can be obtained that complement established modeling approaches. The chapter concludes by pointing toward some potentially fruitful areas of agent-based macroeconomic research.
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10

P, Mermet Jean, ed. VHDL for simulation, synthesis, and formal proofs of hardware. Dordrecht: Kluwer Academic, 1992.

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11

Succi, Sauro. Lattice Boltzmann Models for Microflows. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.003.0029.

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The Lattice Boltzmann method was originally devised as a computational alternative for the simulation of macroscopic flows, as described by the Navier–Stokes equations of continuum mechanics. In many respects, this still is the main place where it belongs today. Yet, in the past decade, LB has made proof of a largely unanticipated versatility across a broad spectrum of scales, from fully developed turbulence, to microfluidics, all the way down to nanoscale flows. Even though no systematic analogue of the Chapman–Enskog asymptotics is available in this beyond-hydro region (no guarantee), the fact remains that, with due extensions of the basic scheme, the LB has proven capable of providing several valuable insights into the physics of flows at micro- and nano-scales. This does not mean that LBE can solve the actual Boltzmann equation or replace Molecular Dynamics, but simply that it can provide useful insights into some flow problems which cannot be described within the realm of the Navier–Stokes equations of continuum mechanics. This Chapter provides a cursory view of this fast-growing front of modern LB research.
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12

Spritzgießen 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783182443513.

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Inhalt Plenarvorträge Navigating Complexity – Wie können wir in der „VUCA – World“ nachhaltig erfolgreich sein? Dr. F. Böttcher, Dr. K.-M. Schuhmann, Contarix GmbH, Kelkheim 1 Möglichkeiten und Grenzen der theoretischen DoE – Was entscheidet über gut und schlecht? Dipl.-Ing. (FH) C. Mädje, Gira Giersiepen GmbH & Co. KG, Radevormwald; Dipl.-Ing. (FH) M. Schmellenkamp, SIGMA Engineering GmbH, Aachen 15 Mehrwert in der Praxis – Intelligente Nutzung von Prozess- und Maschinendaten aus der Spritzgießproduktion Dipl.-Ing. I. Brexeler, Krallmann Holding u. Verwaltungs GmbH, Hiddenhausen; Dr.-Ing. S. Kruppa, KraussMaffei Technologies GmbH, München; Prof. Dr.-Ing. R. Schiffers, M.Sc. A. Schulze Struchtrup, Universität Duisburg-Essen 19 Kurzberichte aus der Forschung Von der Simulation in die Maschine – Objektivierte Prozesseinrichtung durch maschinelles Lernen Prof. Dr.-Ing. Ch. Hopmann, Dr.-Ing. M. Theunissen, J. Heinisch, M.Sc., Institut für Kunststoffverarbeitung ...
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13

Automation 2018. VDI Verlag, 2018. http://dx.doi.org/10.51202/9783181023303.

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Inhalt (Auszüge…) Fertigungsautomation – Plant Lifecycle Management Multidimensionale Datenmodellierung und Analyse zur Qualitätssicherung in der Fertigungsautomatisierung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 B. Lindemann, N. Jazdi, M. Weyrich, Institut für Automatisierungstechnik und Softwaresysteme, Universität Stuttgart, Stuttgart Intelligente Qualitätssicherung und Fehlerursachenanalyse für Spritzgießmaschinen . . . . . . 13 A. Diedrich, J. Eickmeyer, O. Niggemann, Fraunhofer IOSB-INA, Lemgo; R. Preis, Plastikpack GmbH, Steinheim-Bergheim; J. Häfner, Häfner & Krullmann GmbH, Leopoldshöhe Durchgängige Nutzung mechatronischer Anlagenmodelle über den gesamten Lebenszyklus automobiler Produktionsanlagen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 F. Auris, Daimler AG, Ulm; H. Zipper, ifak e.V., Magdeburg; M. Brandl, Algoryx Simulation AB, München; S. Süß, Prof. Dr.-Ing. Christian...
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14

Vigano, Luca, and Margus Veanes. Tests and Proofs: 7th International Conference, TAP 2013, Budapest, Hungary, June 16-20, 2013. Proceedings. Springer, 2013.

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15

Vigano, Luca, and Margus Veanes. Tests and Proofs: 7th International Conference, TAP 2013, Budapest, Hungary, June 16-20, 2013. Proceedings. Springer, 2013.

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16

Baillo, Amparo, Antonio Cuevas, and Ricardo Fraiman. Classification methods for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.10.

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This article reviews the literature concerning supervised and unsupervised classification of functional data. It first explains the meaning of unsupervised classification vs. supervised classification before discussing the supervised classification problem in the infinite-dimensional case, showing that its formal statement generally coincides with that of discriminant analysis in the classical multivariate case. It then considers the optimal classifier and plug-in rules, empirical risk and empirical minimization rules, linear discrimination rules, the k nearest neighbor (k-NN) method, and kernel rules. It also describes classification based on partial least squares, classification based on reproducing kernels, and depth-based classification. Finally, it examines unsupervised classification methods, focusing on K-means for functional data, K-means for data in a Hilbert space, and impartial trimmed K-means for functional data. Some practical issues, in particular real-data examples and simulations, are reviewed and some selected proofs are given.
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