Modelling and Inverse Problems of Control for Distributed Parameter Systems; Proceedings of IFIP(W.G. 7.2)-IIASA Conference, July 24-28, 1989

The techniques of solving inverse problems that arise in the estimation and control of distributed parameter systems in the face of uncertainty as well as the applications of these to mathematical modelling for problems of applied system analysis (environmental issues, technological processes, biomathematical models, mathematical economy and other fields) are among the major topics of research at the Dynamic Systems Project of the Systems and Decision Sciences (SDS) Program at IIASA. In July 1989 the SDS Program was a coorganizer of a regular IFIP (WG 7.2) conference on Modelling and Inverse Problems of Control for Distributed Parameter Systems that was held at IIASA, and was attended by a number of prominent theorists and practitioners. One of the main purpose of this meeting was to review recent developments and perspectives in this field. The proceedings are presented in this volume

Nonlinear Systems

Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems

An evolutionary algorithm approach to ecological optimal control problems

There are several challenges associated with applying conventional (hereafter classic) optimal control (OC) methods to ecological optimal control problems (OCPs). Conditions required by these methods, including differentiability and convexity, for example, are not always met, and ecological problems do not always adhere to solvable OCP formulations. Moreover, mathematically optimal solutions do not always translate to optimal ecological strategies in practice. Despite this, alternative OC approaches are relatively under-explored. Evolutionary algorithms (EAs) circumvent many of the complex aspects of classic OC methods and have been successfully applied to diverse OCPs. Nevertheless, EAs have sel dom been applied to ecological OCPs. The viability of an EA approach to ecological OCPs was therefore investigated in the current study, facilitated by four case studies of increasing complexity and a genetic algorithm (GA) as a representative EA approach. To ascertain the accuracy of a GA approach, comparisons between a GA and classic OC methods were conducted in the first three case studies. The GA generated near-optima in these comparisons, comparable to the corresponding classical solutions, whilst avoiding non-trivial mathematical theory. Supported by these results, an unconventional OCP, that arguably cannot be solved using classic OC methods, was formulated in the fourth case study, and solved using a GA approach. The resulting solution was feasible and further conformed with strategies found to be successful in practice. Additionally, the GA approach was rela tively simple to apply in all case studies. These collective outcomes of demonstrated the viability of a GA as an OC method in eco logical OCPs, thereby supporting the use of an EA approach as an alternative to classic OC methods in ecological OCPs. The feasibility of an EA approach to atypical OCPs was further demonstrated, which may act to increase realism in OC applications. Further investigation in this regard is thus warranted by this study.Thesis (MS) -- Faculty of Science, Mathematics and Applied Mathematics, 202

An evolutionary algorithm approach to ecological optimal control problems

There are several challenges associated with applying conventional (hereafter classic) optimal control (OC) methods to ecological optimal control problems (OCPs). Conditions required by these methods, including differentiability and convexity, for example, are not always met, and ecological problems do not always adhere to solvable OCP formulations. Moreover, mathematically optimal solutions do not always translate to optimal ecological strategies in practice. Despite this, alternative OC approaches are relatively under-explored. Evolutionary algorithms (EAs) circumvent many of the complex aspects of classic OC methods and have been successfully applied to diverse OCPs. Nevertheless, EAs have sel dom been applied to ecological OCPs. The viability of an EA approach to ecological OCPs was therefore investigated in the current study, facilitated by four case studies of increasing complexity and a genetic algorithm (GA) as a representative EA approach. To ascertain the accuracy of a GA approach, comparisons between a GA and classic OC methods were conducted in the first three case studies. The GA generated near-optima in these comparisons, comparable to the corresponding classical solutions, whilst avoiding non-trivial mathematical theory. Supported by these results, an unconventional OCP, that arguably cannot be solved using classic OC methods, was formulated in the fourth case study, and solved using a GA approach. The resulting solution was feasible and further conformed with strategies found to be successful in practice. Additionally, the GA approach was rela tively simple to apply in all case studies. These collective outcomes of demonstrated the viability of a GA as an OC method in eco logical OCPs, thereby supporting the use of an EA approach as an alternative to classic OC methods in ecological OCPs. The feasibility of an EA approach to atypical OCPs was further demonstrated, which may act to increase realism in OC applications. Further investigation in this regard is thus warranted by this study.Thesis (MS) -- Faculty of Science, Mathematics and Applied Mathematics, 202

Mathematical Modeling of Water Quality: Streams, Lakes and Reservoirs

This book is the first to deal comprehensively with the subject of mathematical modeling of water quality in streams, lakes, and reservoirs. About one third of the book is devoted to model development processes -- identification, formulation, parameter estimation, calibration, sensitivity testing, and application -- and a thorough review of the mathematical principles and techniques of modeling. Emphasis is placed on well documented models, representative of the current state of the art, to illustrate capabilities and limitations for the simulation of water quality. About two thirds of the book deals with specific applications of models for simulation of water quality in natural water bodies. Topics covered include modeling of temperature, dissolved oxygen and phytoplankton growth in streams, development and application of one-dimensional models of stratified impoundments, two- and three-dimensional modeling of circulation and water quality in large lakes, thermally stratified plumes and cooling ponds, ecology of lakes and reservoirs, modeling of toxic substances, and the use of models in water quality management and decision making

Uncertainty and Forecasting of Water Quality

This book brings together a number of critical discussions on the role of uncertainty in the development and use of mathematical models for water quality management. It covers the application of recursive estimation, time-series analysis, maximum likelihood estimation, and the Group Method of Data Handling (GMDH), to the problem of model identification. It also treats the analysis of prediction-error propagation, real-time forecasting, and the use of Monte Carlo simulation in the generation of speculative hypotheses about system behaviour

Life Sciences Program Tasks and Bibliography

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1995. Additionally, this inaugural edition of the Task Book includes information for FY 1994 programs. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web pag

Life Sciences Program Tasks and Bibliography for FY 1997

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1997. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive internet web page

Large-Scale Modelling and Interactive Decision Analysis

These Proceedings report the scientific results of an International Workshop attended by more than fifty scientists from thirteen countries. This volume is structured in three parts: (I) Theory and Methodology, (II) Interaction Principles and Computational Aspects and (III) Applications. Part I contains papers dealing with utility and game theory, multicriteria optimizations theory and interactive procedures, dynamic models/systems and concepts of multicriteria analysis. Papers dealing with the user-machine interface, intelligent (user-friendly) decision support and problems of computational aspects are included in Part II. Contributions with applications are mainly concentrated in Part III but can also be found in several papers in other parts. Use of the term "large-scale" in the title of the Proceedings was especially substantiated by contributions dealing with modelling and decision analysis problems of the size of a whole national economy like structuring the carbochemical industry, the energy system or even natural gas trade in Europe