Simulation and modeling of natural processes
- 4.2
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Course Summary
Learn how to model and simulate natural processes in this comprehensive course. From understanding the basics of simulation to applying it to various natural scenarios, this course is perfect for those interested in environmental science, biology, and physics.Key Learning Points
- Understand the basics of modeling and simulation
- Learn how to apply simulation to natural processes
- Explore various scenarios and case studies
- Gain practical skills and knowledge for environmental science, biology, and physics
Related Topics for further study
Learning Outcomes
- Create and apply models to natural processes
- Analyze and interpret simulation data
- Develop practical skills for environmental science, biology, and physics
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of mathematics
- Interest in environmental science, biology, and physics
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Environmental Modeling
- Computational Physics
Related Education Paths
Notable People in This Field
- Dr. Jane Goodall
- Dr. Neil deGrasse Tyson
Related Books
Description
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.
Outline
- 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
Bastien Chopard
4.2 Raiting
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- 0.0
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- 4.1
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- 4.8
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