Geometric Computing


This course will cover mathematical concepts and efficient numerical methods for geometric computing. We will develop and implement algorithms to simulate and optimize 2D and 3D geometric models with an emphasis towards computational design for digital fabrication.


  • Overview of modern digital fabrication technology
  • Discrete geometric models for curves, surfaces, volumes
  • Basics of finite element modeling
  • Physics-based simulation methods
  • Forward and inverse design optimization methods
  • Shape Optimization

Learning Prerequisites

Recommended Courses:

CS-328 : Numerical Methods for Visual Computing and ML

Important concepts to start the course:

Undergraduate knowledge of linear algebra, calculus, and numerical methods; programming experience (e.g., Python, C/C++, Java, Scala)

Learning Outcomes

At the end of the course, students must be able to:

  • Model and formalize geometric shape design & optimization problems.
  • Design and implement computational methods for shape processing, physics-based simulation, and numerical optimization based on discrete geometry representations.
  • Apply geometric abstraction principles to reduce the complexity of shape optimization problems.
  • Assess / Evaluate geometry processing algorithms for their suitability for specific digital fabrication technologies.

Transversal Skills

  • Demonstrate a capacity for creativity.
  • Continue to work through difficulties or initial failure to find optimal solutions.
  • Use both general and domain specific IT resources and tools.

Expected Student Activities

Attend and participate in lectures, study provided reading material, solve theory exercises and implementation homeworks, design and fabricate (with support) physical models.

Assessment Methods

Graded theory and implementation homeworks.

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