UNIGE - Simulation and modeling of natural processes
- Offered byCoursera
Simulation and modeling of natural processes at Coursera Overview
Duration | 23 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Simulation and modeling of natural processes at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 23 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Simulation and modeling of natural processes at Coursera Course details
- 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.
Simulation and modeling of natural processes at Coursera Curriculum
Introduction and general concepts
Objectives and background
Modeling and Simulation
Modeling Space and Time
Example of bio-medical Modeling
Monte Carlo methods I
Monte Carlo methods II
Monte Carlo methods III
Course slides
Introduction and general concepts
Introduction to programming with Python 3
Introduction to high performance computing for modeling
Concepts of code optimization
Concepts of parallelism
Palabos, a parallel lattice Boltzmann solver
An introduction to Python 3
Running a Python program
Variables and data types
Operators
Conditional Statements
Loops
Functions
NumPy
Course slides
Dive into python 3
Introduction to programming with Python 3
Project - Piles
Project - Class:Integration
Dynamical systems and numerical integration
General introduction to dynamical systems
The random walk
Growth of a population
Balance equations I
Balance equations II
Integration of ordinary differential equations
Error of the approximation
The implicit Euler scheme
Numerical integration of partial differential equations
Course slides
References for numerical analysis
A reference for the random walk
Dynamical systems and numerical integration
The implicit Euler scheme
Project - Lotka-Volterra
Cellular Automata
Definition and basic concepts
Historical background
A mathematical abstraction of reality
Cellular Automata Models for Traffic
Complex systems
Lattice-gas models
Microdynamics of LGA
Course slides
Notes on the Parity Rule
Cellular Automata
Project - The Parity Rule
Lattice Boltzmann modeling of fluid flow
Computational Fluid Dynamics: Overview
Equations and challenges
From Lattice Gas to Lattice Boltzmann
Macroscopic Variables
Collision step: the BGK model
Streaming Step
Boundary Conditions
Flow around an obstacle
Course slides
Optional - Equations and challenges
Lattice Boltzmann modeling of fluid flow
Project - Flow around a cylinder
Collision Invariant
Particles and point-like objects
Particles and point-like objects: Overview
Newton?s laws of motion, potentials and forces
Time-integration of equations of motion
The Lennard-Jones potential: Introducing a cut-off distance
The n-body problem: Evaluation of gravitational forces
Barnes-Hut algorithm: using the quadtree
Course slides
Particles and point-like objects
Project - Barnes-Hut Galaxy Simulator
Introduction to Discrete Events Simulation
Introduction to Discrete Events
Definition of Discrete Events Simulations
Optimisation problems
Implementation matters
Traffic intersection
Volcano ballistics
Course slides
Introduction to Discrete Event Simulation
Project - Simple modelling of traffic lights
Agent based models
Motivation
Agents
Multi-Agent systems
Implementation of Agent Based Models
Ants Corpse clustering
Bacteria chemotaxy
Course slides
Agent based models
Project - Multi-agents model
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