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Bastien Chopard,
Jean-Luc Falcone,
Jonas Latt,
and
Orestis Malaspinas
This course gives you an introduction to modeling methods and simulation tools for a wide range of natural phenomena. The different methodologies that will be presented here can be applied to very wide range of topics such as fluid motion, stellar dynamics, population evolution, ... This course does not intend to go deeply into any numerical method or process and does not provide any recipe for the resolution of a particular problem. It is rather a basic guideline towards different methodologies that can be applied to solve any kind of problem and help you pick the one best suited for you.
Read more
This course gives you an introduction to modeling methods and simulation tools for a wide range of natural phenomena. The different methodologies that will be presented here can be applied to very wide range of topics such as fluid motion, stellar dynamics, population evolution, ... This course does not intend to go deeply into any numerical method or process and does not provide any recipe for the resolution of a particular problem. It is rather a basic guideline towards different methodologies that can be applied to solve any kind of problem and help you pick the one best suited for you.
Read more
This course gives you an introduction to modeling methods and simulation tools for a wide range of natural phenomena. The different methodologies that will be presented here can be applied to very wide range of topics such as fluid motion, stellar dynamics, population evolution, ... This course does not intend to go deeply into any numerical method or process and does not provide any recipe for the resolution of a particular problem. It is rather a basic guideline towards different methodologies that can be applied to solve any kind of problem and help you pick the one best suited for you.
The assignments of this course will be made as practical as possible in order to allow you to actually create from scratch short programs that will solve simple problems. Although programming will be used extensively in this course we do not require any advanced programming experience in order to complete it.
Register for this course and see more details by visiting:
OpenCourser.com/course/7mmxop/simulation
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What's inside
Syllabus
Introduction and general concepts
This module gives an overview of the course and presents the general ideas about modeling and simulation. An emphasis is given on ways to represent space and time from a conceptual point of view. An insight of modeling of complex systems is given with the simulation of the grothw and thrombosis of giant aneurysms. Finally, a first class of modeling approaches is presented: the Monte-Carlo methods.
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Syllabus
Introduction and general concepts
This module gives an overview of the course and presents the general ideas about modeling and simulation. An emphasis is given on ways to represent space and time from a conceptual point of view. An insight of modeling of complex systems is given with the simulation of the grothw and thrombosis of giant aneurysms. Finally, a first class of modeling approaches is presented: the Monte-Carlo methods.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides an introduction to a wide range of modeling methods and simulation tools applicable in fields like fluid motion, stellar dynamics, population evolution, and more
Offers a strong foundation for learners interested in exploring modeling and simulation approaches to solve problems in various domains
Taught by instructors with expertise in high-performance computing and modeling, including Bastien Chopard, Jean-Luc Falcone, Jonas Latt, and Orestis Malaspinas
Requires no advanced programming experience, making it accessible to learners with diverse backgrounds
Includes practical assignments that allow learners to apply the concepts and create simple programs to solve problems
Covers fundamental concepts in programming with Python 3, providing a basis for further exploration in the field
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Reviews summary
Overview of simulation and modeling methods
According to learners, this course offers a broad overview of various simulation and modeling methods for natural processes. Many find the practical programming assignments in Python to be a valuable way to apply concepts. However, a frequent point raised is that the difficulty level of the programming tasks is higher than expected for true beginners, suggesting that prior coding experience is beneficial despite the course description. Students note the course provides a solid theoretical foundation but due to its wide scope, it doesn't dive deeply into any single method. Overall, the course is seen as highly valuable for those with a quantitative background seeking an introduction to the field, though some find the pacing fast or certain explanations could be clearer.
Covers many methods without deep specialization.
"...doesn't go super deep into any one method."
"Covers many methods superficially."
"Wish it focused on fewer methods in more detail."
Benefits greatly from prior math/physics/coding knowledge.
"It definitely requires a solid math/physics background to fully grasp the concepts..."
"Good prerequisite is probably university-level calculus and some basic programming experience."
"Best suited for those with a quantitative background."
"Having a background in physics or engineering helps immensely."
Hands-on coding reinforces theoretical concepts.
"The Python modules are excellent for getting hands-on experience."
"The assignments really help cement understanding."
"The Python exercises are practical..."
"The programming assignments are key to grasping the concepts."
Provides a wide introduction to simulation methods.
"This course provides a fantastic overview of various simulation methods."
"It covers a wide range of topics, which is a strength..."
"Solid course covering a broad range of simulation techniques."
"A comprehensive survey of simulation techniques. It's great for getting an idea of what methods exist."
Assignments are difficult, require prior coding.
"The programming aspect was harder than expected."
"assignments are very difficult if you don't have prior coding experience."
"I struggled with the coding parts even with some prior experience."
"The Python intro is too basic if you've never coded before."
"The Python section is too brief for absolute beginners."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Simulation and modeling of natural processes with these
activities:
Python Tutorial for Beginners
Show steps
Get acquainted with the basics of Python programming by following this step-by-step beginner-friendly tutorial.
Show steps
-
Access the chosen tutorial and follow the instructions.
Read 'Numerical Recipes'
Show steps
Expand your knowledge of numerical methods by exploring this classic reference on computational techniques.
View
Numerical Recipes in C++
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read selected chapters relevant to the course topics.
-
Work through the exercises provided in the book.
Assist Peers in a Study Group
Show steps
Reinforce your understanding of course concepts by actively assisting peers in a study group setting.
Show steps
-
Join or form a study group with other course participants.
-
Prepare for study sessions by reviewing course materials.
-
Facilitate discussions, answer questions, and provide support to group members.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Python Coding Challenges
Show steps
Test your Python skills and solidify your understanding through hands-on problem-solving challenges.
Show steps
-
Select a reputable online platform or book with Python coding challenges.
-
Choose a coding challenge that corresponds to your knowledge level.
-
Develop a solution to the challenge on your own.
-
Review your solution, identify errors, and make necessary adjustments.
Create a Presentation on a Simulation Technique
Show steps
Deepen your understanding of a chosen simulation technique by creating a presentation that explains its principles and applications.
Show steps
-
Select a simulation technique covered in the course.
-
Research the technique thoroughly.
-
Create a presentation using slides or a visual tool.
-
Include a demonstration of the technique's practical applications.
Contribute to an Open-Source Project Related to Modeling or Simulation
Show steps
Gain practical experience and contribute to the wider modeling and simulation community by participating in an open-source project.
Show steps
-
Identify an open-source project that aligns with your interests.
-
Familiarize yourself with the project's codebase and documentation.
-
Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements.
Python Tutorial for Beginners
Show steps
Get acquainted with the basics of Python programming by following this step-by-step beginner-friendly tutorial.
Show steps
Show steps
Show steps
- Access the chosen tutorial and follow the instructions.
Read 'Numerical Recipes'
Show steps
Expand your knowledge of numerical methods by exploring this classic reference on computational techniques.
View
Numerical Recipes in C++
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read selected chapters relevant to the course topics.
- Work through the exercises provided in the book.
Assist Peers in a Study Group
Show steps
Reinforce your understanding of course concepts by actively assisting peers in a study group setting.
Show steps
Show steps
Show steps
- Join or form a study group with other course participants.
- Prepare for study sessions by reviewing course materials.
- Facilitate discussions, answer questions, and provide support to group members.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Python Coding Challenges
Show steps
Test your Python skills and solidify your understanding through hands-on problem-solving challenges.
Show steps
Show steps
Show steps
- Select a reputable online platform or book with Python coding challenges.
- Choose a coding challenge that corresponds to your knowledge level.
- Develop a solution to the challenge on your own.
- Review your solution, identify errors, and make necessary adjustments.
Create a Presentation on a Simulation Technique
Show steps
Deepen your understanding of a chosen simulation technique by creating a presentation that explains its principles and applications.
Show steps
Show steps
Show steps
- Select a simulation technique covered in the course.
- Research the technique thoroughly.
- Create a presentation using slides or a visual tool.
- Include a demonstration of the technique's practical applications.
Contribute to an Open-Source Project Related to Modeling or Simulation
Show steps
Gain practical experience and contribute to the wider modeling and simulation community by participating in an open-source project.
Show steps
Show steps
Show steps
- Identify an open-source project that aligns with your interests.
- Familiarize yourself with the project's codebase and documentation.
- Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements.
Career center
Learners who complete Simulation and modeling of natural processes will develop knowledge and skills
that may be useful to these careers:
Computational Physicist
Computational Physicist
Computational physicists use computational methods to study the properties of matter and solve problems in physics. This course can be useful for those who want to work in this field, as it teaches students how to simulate and model natural phenomena. This course provides an introduction to modeling methods and simulation tools that can be used to study a wide range of natural phenomena, such as fluid motion, stellar dynamics, and population evolution. The course also covers topics such as dynamical systems, cellular automata, and particle-based modeling, which are all important techniques used in computational physics.
Data Scientist
Data Scientist
Data scientists collect, analyze, and interpret data to help organizations make informed decisions. This course can be useful for those who want to work in this field, as it teaches students how to use Python to program, which is a popular programming language for data science. The course also teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them, which are skills that are useful for data scientists who work with complex data.
Financial Analyst
Financial Analyst
Financial analysts use financial data to make investment recommendations. This course can be useful for those who want to work in this field, as it teaches students how to use mathematics and statistics to analyze data. The course also teaches students how to use Python to program, which is a popular programming language for financial analysis.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical models to solve business problems. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for operations research.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. This course can be useful for those who want to work in this field, as it teaches students how to use Python to program. The course also teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them, which are skills that are useful for software engineers who work on complex software systems.
Systems Analyst
Systems Analyst
Systems analysts design and implement computer systems. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for systems analysis.
Teacher
Teacher
Teachers teach students about a variety of subjects. This course can be useful for those who want to teach mathematics or science, as it provides an introduction to modeling methods and simulation tools that can be used to teach these subjects. The course also teaches students how to use Python to program, which is a popular programming language for teaching.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for actuarial science.
Biostatistician
Biostatistician
Biostatisticians use statistics to analyze biological data. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for biostatistics.
Economist
Economist
Economists use economic theory and data to analyze the economy. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for economics.
Epidemiologist
Epidemiologist
Epidemiologists study the causes and spread of disease. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for epidemiology.
Market Researcher
Market Researcher
Market researchers study the market for products and services. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for market research.
Operations Manager
Operations Manager
Operations managers oversee the operations of an organization. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for operations management.
Product Manager
Product Manager
Product managers oversee the development and marketing of products. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for product management.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to make investment decisions. This course can be useful for those who want to work in this field, as it teaches students how to use mathematical equations to represent natural phenomena and how to numerically solve them. The course also teaches students how to use Python to program, which is a popular programming language for quantitative analysis.
For more career information including salaries, visit:
OpenCourser.com/course/7mmxop/simulation
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Simulation and modeling of natural processes.
This textbook covers a range of scientific and engineering computing topics and can be used as a reference guide. The author introduces important theoretical concepts and demonstrates computational practices using Python programming.
Provides an introduction to high-performance computing including chapters on numerical integration, linear systems, Eigenproblems, and nonlinear equations. This book can be used as a reference guide for Python programming and the included problems and exercises are useful for practice.
This textbook serves as a detailed introduction to dynamical systems containing many practice exercises.
Provides a practical approach to CFD, covering the basics of the field and including practical examples of CFD calculations. It is also suitable for classroom use.
This reference book collection of algorithms and routines for scientific computing. It includes many topics that may be useful in simulation and modeling.
Provides a detailed introduction to data science and machine learning using the Python programming language.
Provides an introduction to simulation modeling using the Arena software.
Provides an introduction to HPC for scientists and engineers, covering topics such as parallel programming, performance analysis, and code optimization.
Provides an introduction to bio-inspired computing techniques, which may be useful for simulating biological systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/7mmxop/simulation
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Rating
4.3
Effort
23 hours to complete
Via
Coursera
Institution
University of Geneva
Instructors
Bastien Chopard
Jean-Luc Falcone
Jonas Latt
Orestis Malaspinas
Language
English
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