Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.

Last update: Jul 21, 2022

Overview

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
Visualizing soft-system dynamics

Setup

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

Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.

Related tags

Overview

Computational Design and Dynamics of Soft Systems ·

Description

Prerequisities

Content

Organization

Setup

Owner

Tejaswin Parthasarathy

This folder contains the python code of UR5E's advanced forward kinematics model.

Cmsc11 arcade - Final Project for CMSC11

An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

Accuracy Aligned. Concise Implementation of Swin Transformer

Mesh Graphormer is a new transformer-based method for human pose and mesh reconsruction from an input image

Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

Show-attend-and-tell - TensorFlow Implementation of "Show, Attend and Tell"

Matching python environment code for Lux AI 2021 Kaggle competition, and a gym interface for RL models.

Detection of drones using their thermal signatures from thermal camera through YOLO-V3 based CNN with modifications to encapsulate drone motion

Fine-Tune EleutherAI GPT-Neo to Generate Netflix Movie Descriptions in Only 47 Lines of Code Using Hugginface And DeepSpeed

A Re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"

The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

Code for "ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on", accepted at WACV 2021 Generation of Human Behavior Workshop.

[CVPR 2021] Few-shot 3D Point Cloud Semantic Segmentation

2021:"Bridging Global Context Interactions for High-Fidelity Image Completion"

arxiv-sanity, but very lite, simply providing the core value proposition of the ability to tag arxiv papers of interest and have the program recommend similar papers.

A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset

Detecting Blurred Ground-based Sky/Cloud Images

Code for CPM-2 Pre-Train

Segment axon and myelin from microscopy data using deep learning