Computational Design and Dynamics of Soft Systems ·

This is a repository that contains the source code for generating the lecture notes, handouts, exercises for the computational lab-sessions of the course offered at UIUC.

Description

This course provides a hands-on introduction to modern modeling and simulations techniques for heterogeneous structures made of assemblies of soft, elastic slender elements. Such systems are ubiquitous in nature, from animal musculoskeletal architectures to ‘birds-nest’ composite materials. They are also becoming increasingly relevant in robotics. Students will implement in python their own Cosserat rods-based solver. The developed solver will be then coupled with evolutionary optimization techniques for design, and reinforcement learning for control.

Prerequisities

None.

Content

• Introduction to modeling and simulation for inverse design
• Basics of evolutionary strategies
• Covariance Matrix Adaptation – Evolution Strategy (CMA-ES)
• Basic concepts of Reinforcement Learning
• Soft robotic modeling with Cosserat rods
• Space and time discretization
• Application to snake slithering
• Complex creatures modeling
• Examples of potential experimental applications

Organization

The course is organized in three modules listed below.

• Python for engineers
  • Crash course in Python for engineers
    • Scientific computing via Python
• Non-linear stochastic optimization
  • Implementing CMA-es for nonlinear stochastic optimization
    • Adopting CMA-es to tackle real-life inverse-design problems
• Modeling of soft systems
  • Rotational dynamics of slender rods and its numerical resolution
  • Temporal dynamics of soft systems and its numerical resolution
  • Spatial dynamics of soft systems and its numerical resolution
    • Putting the components together
• Visualizing soft-system dynamics

Setup

To get started with the course, please consult this folder.