soft_systems_course
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (9.9%) to scientific vocabulary
Keywords
Repository
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
Basic Info
- Host: GitHub
- Owner: tp5uiuc
- License: other
- Language: Jupyter Notebook
- Default Branch: master
- Homepage: https://parthas1.github.io/teaching/
- Size: 20.3 MB
Statistics
- Stars: 7
- Watchers: 1
- Forks: 3
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
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.
Owner
- Name: Tejaswin Parthasarathy
- Login: tp5uiuc
- Kind: user
- Company: University of Illinois
- Website: parthas1.github.io
- Repositories: 5
- Profile: https://github.com/tp5uiuc
💻 HPC + Software | 🔬Simulation + Algorithms | 📚 Continuum mechanics | 🤖 AI
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- jupyter ==1.0.0
- matplotlib ==3.1.1
- numpy ==1.17.2
- scipy ==1.3.1