Engineering Systems Dynamics Modelling
Budget: $2 – $8 USD
Modelling of Complex Systems
Should have basic statistical experience at the bachelor level, including descriptive statistics, correlation measures, probability distributions such as normal and binomial distribution, basics of probability theory.
Should know fundamentals of ordinary differential equations as taught at the bachelor level.
Will have to complete an entry self-test (Moodle) in advance. Preparatory material is provided
Must install a systems dynamics software and get acquainted with the software prior
Objective
• describe different aspects of system theory and assess where and how system theory is applied to real-world problems.
• use a mathematical tool (Vensim) to model and simulate a dynamical system.
• derive a system formulation from ordinary differential equations (e.g. chemical reaction).
• perform parametric studies with the Monte-Carlo method and apply optimization techniques to fit model predictions to experimental findings.
• model, analyze, justify and communicate a system autonomously.
Introduces basic mathematical tools and software used for the modeling and analysis of real-world systems in the context of life sciences
Introduction into system theory / system dynamics
- What is a complex system? What is its purpose?
- Overview and characterization of various systems (static/dynamical systems,
discrete and continuous systems)
- Introduction to mathematical models used for the modeling and analysis of
systems, including differential equations.
- Properties of linear, non-linear and chaotic systems
- Qualitative methods for analyzing system models (graphs, feedback, active-passive Matrix, Vester’s paper computer)
• Introduction into tools and methods used for system analysis and modeling
- Basic modeling using software tools (e.g. Vensim, Excel)
- Control structures, Look-ups, data sampling, functions
- Analysis of equilibrium and stationary states
- Numerical integration methods
- Introduction to stability analysis and convergence testing
- Level of validity and detection of simulation-inherent errors
• Advanced system dynamics techniques
- Parameter optimization for fitting model behavior to experimental data
- Monte-Carlo simulation to perform parametric sensitivity studies
• Detailed case studies of systems and their modeling with examples from
biomechanics, environmental sciences, biology, chemistry, industrial processes, and economics, e.g. plant dynamics, bacterial population behavior, drug reactions, or buyer/seller market dynamics
• Practical communication and documentation of a model and of simulation results - argumentation and motivation of a model logic
- visualization of the model structure and its behavior
- formulation of hypothesis and testing by means of simulation
Conceive and develop an own case study, develop an own model as a case study (practical study). The individual projects, two individual presentations are required to deliver
Project duration CW34 to Calendar week 44 19th of August to 31st of October 2024
Following material will be used:
Course Book
H. Bossel, Systems and Models, 2007, ISBN 978-3-8334-8121-5
Introductory material
R. L. Flood, E. R. Carson, Dealing with Complexity: An Introduction to the Theory and
Application of Systems Science, Springer, 1993
http://en.wikipedia.org/wiki/Systems_thinking
D. Aronson, Overview of Systems Thinking,
http://www.thinking.net/Systems_Thinking/OverviewSTarticle.pdf
K. North, An Introduction to Systems Thinking,
http://courses.umass.edu/plnt597s/KarlsArticle.pdf
Only serious who understand the requirements should contact. with their complete details
Should have basic statistical experience at the bachelor level, including descriptive statistics, correlation measures, probability distributions such as normal and binomial distribution, basics of probability theory.
Should know fundamentals of ordinary differential equations as taught at the bachelor level.
Will have to complete an entry self-test (Moodle) in advance. Preparatory material is provided
Must install a systems dynamics software and get acquainted with the software prior
Objective
• describe different aspects of system theory and assess where and how system theory is applied to real-world problems.
• use a mathematical tool (Vensim) to model and simulate a dynamical system.
• derive a system formulation from ordinary differential equations (e.g. chemical reaction).
• perform parametric studies with the Monte-Carlo method and apply optimization techniques to fit model predictions to experimental findings.
• model, analyze, justify and communicate a system autonomously.
Introduces basic mathematical tools and software used for the modeling and analysis of real-world systems in the context of life sciences
Introduction into system theory / system dynamics
- What is a complex system? What is its purpose?
- Overview and characterization of various systems (static/dynamical systems,
discrete and continuous systems)
- Introduction to mathematical models used for the modeling and analysis of
systems, including differential equations.
- Properties of linear, non-linear and chaotic systems
- Qualitative methods for analyzing system models (graphs, feedback, active-passive Matrix, Vester’s paper computer)
• Introduction into tools and methods used for system analysis and modeling
- Basic modeling using software tools (e.g. Vensim, Excel)
- Control structures, Look-ups, data sampling, functions
- Analysis of equilibrium and stationary states
- Numerical integration methods
- Introduction to stability analysis and convergence testing
- Level of validity and detection of simulation-inherent errors
• Advanced system dynamics techniques
- Parameter optimization for fitting model behavior to experimental data
- Monte-Carlo simulation to perform parametric sensitivity studies
• Detailed case studies of systems and their modeling with examples from
biomechanics, environmental sciences, biology, chemistry, industrial processes, and economics, e.g. plant dynamics, bacterial population behavior, drug reactions, or buyer/seller market dynamics
• Practical communication and documentation of a model and of simulation results - argumentation and motivation of a model logic
- visualization of the model structure and its behavior
- formulation of hypothesis and testing by means of simulation
Conceive and develop an own case study, develop an own model as a case study (practical study). The individual projects, two individual presentations are required to deliver
Project duration CW34 to Calendar week 44 19th of August to 31st of October 2024
Following material will be used:
Course Book
H. Bossel, Systems and Models, 2007, ISBN 978-3-8334-8121-5
Introductory material
R. L. Flood, E. R. Carson, Dealing with Complexity: An Introduction to the Theory and
Application of Systems Science, Springer, 1993
http://en.wikipedia.org/wiki/Systems_thinking
D. Aronson, Overview of Systems Thinking,
http://www.thinking.net/Systems_Thinking/OverviewSTarticle.pdf
K. North, An Introduction to Systems Thinking,
http://courses.umass.edu/plnt597s/KarlsArticle.pdf
Only serious who understand the requirements should contact. with their complete details