Computational systems thinking for sustainable eng.

Summary

This course integrates systems thinking and network analysis through theory and computing. The objective of this course is to develop expertise in computationally analyzing and modeling complex systems in civil and urban systems engineering, with a particular emphasis on advancing sustainability.

Content

Keywords

Systems thinking, system dynamics, network analysis, computational modeling, sustainability

Learning Prerequisites

Required courses

Introduction to machine learning for engineers (CIVIL-226); Linear algebra (MATH-111 or similar)

Important concepts to start the course

Coding in Python, background in calculus and linear algebra

Learning Outcomes

By the end of the course, the student must be able to:

• Explain what comprises a complex system in the built environment
• Model complex urban systems and system dynamics
• Explain the characteristics of graphs and networks
• Use network analysis to describe complex systems
• Develop and model strategies for intervening in systems to advance sustainability objectives

Transversal skills

• Communicate effectively with professionals from other disciplines.
• Take account of the social and human dimensions of the engineering profession.
• Demonstrate the capacity for critical thinking
• Make an oral presentation.
• Write a scientific or technical report.

Teaching methods

Lectures, exercises, and course project

Expected student activities

Attend lectures, participate in class discussions and activities, complete exercises, and complete the course project

Assessment methods

2 exams during the semester (40%)
Exercises (20%)
Course project (40%)

Supervision

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

• Thinking in Systems: A Primer, Donella H. Meadows, 2008
• Networks, 2nd Edition, Mark Newman, 2018

Ressources en bibliothèque

• Networks / Newman (2nd ed.)
• Thinking in Systems / Meadows

Moodle Link

• https://go.epfl.ch/CIVIL-534