Simulation and modeling of natural processes
Master computational modeling techniques for natural phenomena using Python. Learn Monte Carlo, cellular automata, and particle-based methods.
Master computational modeling techniques for natural phenomena using Python. Learn Monte Carlo, cellular automata, and particle-based methods.
This comprehensive course introduces various modeling methods and simulation tools for natural phenomena. It covers a wide range of topics from fluid dynamics to population evolution, focusing on practical implementation using Python. The course provides fundamental understanding of different methodologies including Monte Carlo methods, cellular automata, lattice Boltzmann modeling, particle systems, and agent-based models. While programming is extensively used, the course maintains accessibility for those without advanced programming experience, making complex simulation concepts approachable through hands-on practice.
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What you'll learn
Master various modeling methodologies for natural phenomena
Implement simulations using Python programming
Understand particle-based and cellular automata approaches
Apply lattice Boltzmann methods for fluid dynamics
Develop agent-based models for complex systems
Create discrete event simulations
Skills you'll gain
This course includes:
11.5 Hours PreRecorded video
19 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 8 modules in this course
This course provides a comprehensive introduction to computational modeling and simulation of natural processes. Students learn various methodologies including Monte Carlo methods, cellular automata, lattice Boltzmann modeling, and agent-based systems. The curriculum combines theoretical foundations with practical Python programming exercises, covering applications in fluid dynamics, population dynamics, and complex systems simulation.
Introduction and general concepts
Module 1 · 2 Hours to complete
Introduction to programming with Python 3
Module 2 · 3 Hours to complete
Dynamical systems and numerical integration
Module 3 · 3 Hours to complete
Cellular Automata
Module 4 · 3 Hours to complete
Particles and point-like objects
Module 6 · 2 Hours to complete
Introduction to Discrete Events Simulation
Module 7 · 2 Hours to complete
Agent based models
Module 8 · 2 Hours to complete
Fee Structure
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Instructors
Expert in Complex Systems Modeling and Simulation
Bastien Chopard is a full professor at the University of Geneva and a group leader at the Swiss Institute of Bioinformatics. He earned his PhD in theoretical physics from the University of Geneva in 1988 and subsequently completed postdoctoral work at MIT's Laboratory for Computer Science and the Research Center Jülich in Germany. His research focuses on the modeling and simulation of complex systems, and he is widely recognized for his pioneering contributions to Cellular Automata and Lattice Boltzmann methods. With over 300 interdisciplinary scientific publications, his work spans fields such as physics, social and environmental sciences, biomedical applications, parallel computing, and multiscale modeling.
Senior Research Associate and Lecturer at the University of Geneva
Jean-Luc Falcone is a senior research associate and lecturer at the University of Geneva, where he specializes in the intersection of biology and computer science. He earned an interdisciplinary PhD in 2008, focusing on these fields, and has since held the position of HPC (High-Performance Computing) application analyst at CADMOS (Center for the Advanced Modelling of Science). His research interests encompass bioinformatics, multi-science models, multi-scale systems, and parallel computing, making significant contributions to the understanding of natural processes through simulation and modeling.In addition to his research, Dr. Falcone teaches the course "Simulation and Modeling of Natural Processes" on Coursera. This course provides learners with insights into how computational techniques can be applied to simulate and analyze complex natural systems, bridging the gap between theoretical knowledge and practical application in environmental science.
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