Advances in Simulation – serie

Computer simulation has developed into a powerful tool for problem solving in a variety of areas, in the sciences as well as in industrial environments. New developments such as parallel simulation techniques will further improve the efficiency of the tool. Decision support systems, either based on mathematical models or on knowledge based expert systems will make computer simulation accessible to more users, and will provide better environments for systems analysis, modeling and simulation. Systems Analysis and Simulation presents the papers accepted for the 3rd International Symposium for Systems Analysis and Simulation held in Berlin (GDR) in September of 1988. The contributions selected for this two-volume set present the state of the art and current trends in computer simulation. Volume I emphasizes the theoretical foundations and the methodology for computer simulation and systems analysis. Volume II presents a variety of applications in fields such as manufacturing, robotics, economics, and biology.

The present volume contains the papers which were accepted for presentation at the 3rd International Symposium for Systems Analysis and Simulation held in Berlin (GDR), September 12-:-16, 1988. It is already a tradition to meet a broad international community of experts in systems analysis, modelling and simulation at this symposium. This fact shows the requirements for a forum of presentation and discussion of new developments and applications of modelling and simulation in systems analysis. To realize the great interest in this field one has to take into consideration the developed role of computer simulation as a powerful tool of problem solving. More and more areas in sciences and production have been investigated by mathematical models and computer' simulation. Biological sciences and social sciences are even by now influenced by this trend. The model use on the computer has been very much improved in decision support systems. Parallel simulation will provide drastic shortening of computing time. Parallel simulation and model based decision support systems are brought in the focus of international activities. Numerical mathematics, systems theory and control sciences provide with algorithms supporting the modelling process itself based on simulation or analytic methods. Such simulation systems equipped with tools for modelling and graphics for representing results are real model support systems. A new important impact comes from artificial intelligence by knowledge processing. Expert systems may help decision making in case of missing mathematical models. Expert systems may also support teaching and using simulation systems.

This book presents a collection of invited contributions, each reflecting an area of biomedicine in which simulation techniques have been successfully applied. Thus, it provides a state-of-the-art survey of simulation techniques in a variety of biomedical applications. Chapter one presents the conceptual framework for advanced simulations such as parallel processing in biological systems. Chapter two focuses on structured biological modeling based on the bond graph method. This is followed by an up-to-date account of advanced simulation of a variety of sophisticated biomedical processes. The authors provide many insights into how computer simulation techniques and tools can be applied to research problems in biomedicine. The idea for this book arose out of the daily work by experts in their field and reflects developing areas. Therefore, I think the material is timely and hope that the work described will be an encouragement for others. It is the objective of this book to present advanced simulation techniques in biomedicine and outline current research, as well as to point out open problems, in this dynamic field. Finally, I wish to express my thanks to those colleagues who have made this book possible with their contributions.

Knowledge-Based Simulation: Methodology and Application represents a recent compilation of research material that reviews fundamental concepts of simulation methodology and knowledge-based simulation applications. Knowledge-based simulation represents a new and exciting bridge area linking the fields of computer simulation and artificial intelligence. This book will appeal to both theorists and practitioners who require simulation to solve complex problems. A primary attraction of the book is its emphasis on both methodology and applications. In this way, the reader can explore new methods for encoding knowledge-inten- sive information into a simulation model, and new applications that utilize these methods.

Recently there has been considerable interest in qualitative methods in simulation and mathematical model- ing. Qualitative Simulation Modeling and Analysis is the first book to thoroughly review fundamental concepts in the field of qualitative simulation. The book will appeal to readers in a variety of disciplines including researchers in simulation methodology, artificial intelligence and engineering. This book boldly attempts to bring together, for the first time, the qualitative techniques previously found only in hard-to-find journals dedicated to single disciplines. The book is written for scientists and engineers interested in improving their knowledge of simulation modeling. The "qualitative" nature of the book stresses concepts of invariance, uncertainty and graph-theoretic bases for modeling and analysis.