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About 10,000 papers for “Modeling and simulation”

Modeling and simulation

Vogt, William G., Pittsburgh Conference on Modeling and Simulation 1979 Pittsburgh, Pa. · 1991

Cited by 1

Modeling and Simulation

Sumati Kumari Panda, V. Vijayakumar, Ravi P. Agarwal · Synthesis lectures on mathematics and statistics · 2025

Cited by 0

Modeling and Simulation

José L. Encarnação, Georgios Sakas, Heinz‐Otto Peitgen et al. · Beiträge zur graphischen Datenverarbeitung · 1992

Cited by 4

Modeling and Simulation

2008

This chapter contains sections titled: Introduction The Runge–Kutta Method Kinetics of Cell Growth Simulation of a Batch Ethanol Fermentation Ethanol Case Study Luedeking–Piret Model Continuous Stirred-Tank Bioreactor Batch Fermentor versus Chemostat References Homework Problems

Cited by 3

Modeling and Simulation

2018

This chapter shows a general modeling process for model development. Mathematical modeling is a process of writing mathematical expressions describing the behavior of a physical system. Dimensional analysis is an important tool often used to increase the accuracy and efficiency of experimental design. The most common types of similarity are geometric, kinematic, and dynamic. Aerodynamic testing can be conducted in either a wind tunnel or a water tunnel, depending on the facility available and the information required. There are basically three finite-difference expressions used in the finite-difference method: the forward, backward, and central difference expressions. A discrete event simulation model is basically a computer program that can be used to simulate system behavior. An engineer developing a simulation model has at his or her disposal various special purpose simulation languages such as Simscript, Simula, or the General Purpose Simulation System (GPSS).

Cited by 0

Modeling and Simulation

Hans‐Georg Matuttis, Jian Chen · 2014

The relation between experimental and theoretical (both analytical and computational) findings is discussed. The drawbacks of approaches other than the discrete element method (continuum mechanical, lattice and Monte Carlo simulations) are outlined.

Cited by 13

Modeling and simulation

Gabro Smiljanić, Mirsad Todorovac · 1997

Pregled svega onoga sto se radilo na FER-u u podrucju modeliranja i simuliranja, kao i mnostvo radova povezanih s time.

Cited by 0

Simulation and Modeling

Asim El Sheikh, Abid Thyab Al Ajeeli, Evon Abu-Taieh · IGI Global eBooks · 2008

"This book offers insight into the computer science aspect of simulation and modeling while integrating the business practices of SM. It includes current issues related to simulation, such as: Web-based simulation, virtual reality, augmented reality, and artificial intelligence, combining different methods, views, theories, and applications of simulations in one volume" - Provided by publisher

Cited by 41

Modeling and Simulation

Andreas Erdmann · 2018

CONTENTS 31.1 Introduction into Lithography Simulation 65031.1.1 Aerial and Resist Image Formation 651 31.1.2 Physical/Chemical Modifications during the Processing of thePhotoresist 654 31.1.3 General Simulation Strategies 65531.2 Rigorous Modeling of Light Diffraction from Masks 656 31.2.1 Motivation 656 31.2.2 General Remarks on Rigorous Electromagnetic Field Solvers 658 31.2.3 Finite-Difference Time-Domain Method 66031.2.3.1 A Short History of FDTD 660 31.2.3.2 The FDTD Algorithm 66031.2.4 Coupling with Standard Lithography Simulations and Performance Considerations 66731.2.5 Field Decomposition Techniques and other Enhancements 669 31.3 Application of Simulation to Different Mask Types 67031.3.1 Binary Masks 670 31.3.2 Phase Shift Masks 672 31.3.3 EUV-Masks 67531.3.3.1 Efficient Modeling of EUV-Masks 676 31.3.3.2 Mask-Induced Imaging Artifacts in EUV Lithography 67931.4 Simulation of Mask Defects 681 31.4.1 Standard Defect Printability Considerations 681 31.4.2 Rigorous EMF Simulation of Defects on Optical Masks 683 31.4.3 EUV Multilayer Defects 68531.5 Summary 686 Acknowledgments 687 References 688Modeling and simulation have become indispensable tools for the understanding and optimization of lithographic processes and for the development of new process technology. Aerial image simulation is used to evaluate the imaging of designed photomasks and to explore the impact of optical parameters, such as numerical aperture (NA), partial coherence s, and defocus, and wave aberrations on the imaging performance of a projection stepper or scanner. Other simulation approaches are used to describe theimpact of the photoresist thickness, of the post-exposure bake (PEB) temperature and of the development characteristics of the photoresist on the total process performance. Lithography simulation is also used as an effective learning tool for new process engineers, scientists, and managers.

Cited by 4

Modeling and Simulation

Christian Brecher, Abdelhamid Bouabid, M. Deichmueller et al. · Lecture notes in production engineering · 2012

Cited by 2