Simulation and modeling of natural processes from Coursera

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.

Introduction to programming with Python 3

This module intends to provide the most basic concepts of high performance computing used for modeling purposes. It also aims at teaching the basics of Python 3 which will be the programming language used for the quizzes in this course.

Dynamical systems and numerical integration

Dynamical systems modeling is the principal method developed to study time-space dependent problems. It aims at translating a natural phenomenon into a mathematical set of equations. Once this basic step is performed the principal obstacle is the actual resolution of the obtained mathematical problem. Usually these equations do not possess an analytical solution and advanced numerical methods must be applied to solve them. In this module you will learn the basics of how to write mathematical equations representing natural phenomena and then how to numerically solve them.

Cellular Automata

This module defines the concept of cellular automata by outlining the basic building blocks of this method. Then an insight of how to apply this technique to natural phenomena is given. Finally the lattice gas automata, a subclass of models used for fluid flows, is presented.

Lattice Boltzmann modeling of fluid flow

This module provides an introduction to the lattice Boltzmann method, a powerful tool in computational fluid dynamics. The lesson is practice oriented and show, step by step, how to write a program for the lattice Boltzmann method. The program is used to showcase an interesting problem in fluid dynamics, the simulation of a vortex street behind an obstacle.

Particles and point-like objects

A short review of classical mechanics, and of numerical methods used to integrate the equations of motions for many interacting particles is presented. The student will learn that the computational expense of resolving all interaction between particles poses a major obstacle to simulating such a system. Specific algorithms are presented to allow to cut down on computational expense, both for short-range and large-range forces. The module focuses in detail on the Barnes-Hut algorithm, a tree algorithm which is popular a popular approach to solve the N-Body problem.

Introduction to Discrete Events Simulation

In this module, we will see an alternative approach to model systems which display a trivial behaviour most of the time, but which may change significantly under a sequence of discrete events. Initially developed to simulate queue theory systems (such as consumer waiting queue), the Discrete Event approach has been apply to a large variety of problems, such as traffic intersection modeling or volcanic hazard predictions.

Agent based models

Agent Based Models (ABM) are used to model a complex system by decomposing it in small entities (agents) and by focusing on the relations between agents and with the environment. This approach is derived from artificial intelligence research and is currently used to model various systems such as pedestrian behaviour, social insects, biological cells, etc.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Comprehensive simulation and modeling overview

According to students, "Simulation and modeling of natural processes" is a largely positive course that covers a wide variety of engaging assignments, including Python programming exercises and modeling challenges. The course provides a well received introduction to the field, balancing practical applications with theory. However, some learners noted that a strong background in programming, math, and science may be beneficial for fully grasping the material.

Material is presented in a clear and engaging manner.

"Really concise and to-the-point lectures, which encourage you to explore the modelling techniques yourselves."

"The presentation of Lattice Boltzmann methods improved my understanding of inflow and outflow boundaries."

"The videos content from several tutors was very professional."

Course covers many models and simulations for natural processes.

"The course covers multiple aspects of simulations..."

"This is a nice course: many model examples are given."

"Good introduction to various techniques of modeling natural processes."

Coursework includes a mix of Python programming exercises and modeling challenges.

"The build-up during the first 3 weeks moves at quite a reasonable pace."

"I would say that later weeks could be more informative when it comes to material and accessibility of example cases."

"Having said that the amount of example cases overall, for code implementation is very satisfying."

Instructors are knowledgeable but may not be native English speakers.

"It is apparent that the faculy are not native speakers of english."

"They seem like they make up their sentences in their native language and then translate it in their minds to english and then speak."

"Some questions were very difficult to understand because the translation was bad."

Course may be challenging for those without a background in programming, math, or science.

"I learnt a lot in the course, but, I felt that they expected you to have some sort of programming, math or science related knowledge because some topics where taught superficially but the quizes related to that topic where pretty hard in some cases."

"Might be too difficult for people who do not have a solid background in vector calculus, algorithms, and programming."

"Yet if you already have such a background, you are probably already familiar with most of the material covered, as the models discussed in this course are all toy/classroom examples given during a typical undergrad treatment of these CS/Math topics."

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

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Get acquainted with the basics of Python programming by following this step-by-step beginner-friendly tutorial.

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Access the chosen tutorial and follow the instructions.

Read 'Numerical Recipes'

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Expand your knowledge of numerical methods by exploring this classic reference on computational techniques.

View Numerical Recipes in C++ on Amazon

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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

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Reinforce your understanding of course concepts by actively assisting peers in a study group setting.

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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

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Solve Python Coding Challenges

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Test your Python skills and solidify your understanding through hands-on problem-solving challenges.

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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

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Deepen your understanding of a chosen simulation technique by creating a presentation that explains its principles and applications.

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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

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Gain practical experience and contribute to the wider modeling and simulation community by participating in an open-source project.

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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 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.

See salaries and explore the career path for Computational Physicist

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Systems 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.

See salaries and explore the career path for Operations Research 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Epidemiologist

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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Operations Manager