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Books on the topic 'Simulation of proof'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Roever, W. P. de. Data refinement: Model-oriented proof methods and their comparison. 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 University Press, 1998.

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3

Mermet, Jean, ed. VHDL for Simulation, Synthesis and Formal Proofs of Hardware. 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. 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. 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. 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. T

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10

P, Mermet Jean, ed. VHDL for simulation, synthesis, and formal proofs of hardware. 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 fa

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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.

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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-Ber

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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 kern

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