https://github.com/tp5uiuc/soft_systems_course

Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
https://github.com/tp5uiuc/soft_systems_course

cmaes numerical-methods optimization python soft-robotics

Last synced: about 1 month ago
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Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.

• Host: GitHub
• URL: https://github.com/tp5uiuc/soft_systems_course
• Owner: tp5uiuc
• License: other
• Created: 2022-01-04T08:01:50.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2022-07-31T05:10:15.000Z (over 2 years ago)
• Last Synced: 2023-03-11T20:05:51.861Z (almost 2 years ago)
• Topics: cmaes, numerical-methods, optimization, python, soft-robotics
• Language: Jupyter Notebook
• Homepage: https://parthas1.github.io/teaching/
• Size: 20.3 MB
• Stars: 5
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Citation: CITATION.cff

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README

        
Computational Design and Dynamics of Soft Systems

·

[![license](https://img.shields.io/badge/license-MIT-green)](https://mit-license.org/)

=====

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](lectures/01_intro)

	- [Scientific computing via Python](lectures/02_scicomp)

- Non-linear stochastic optimization

  - [Implementing CMA-es for nonlinear stochastic optimization](lectures/03_cma)

	- [Adopting CMA-es to tackle real-life inverse-design

    problems](lectures/03_cma)

- Modeling of soft systems

  - [Rotational dynamics of slender rods and its numerical resolution](lectures/04_elastica)

  - [Temporal dynamics of soft systems and its numerical resolution](lectures/05_timeintegration)

  - [Spatial dynamics of soft systems and its numerical resolution](lectures/06_spaceintegration)

	- [Putting the components together](lectures/07_timespace)

- [Visualizing soft-system dynamics](lectures/08_povray)

## Setup

To get started with the course, please consult [this folder](handouts/00_linux).