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

Interactive dimensionality reduction for large datasets

Where-Got-Time - An NUS timetable generator which uses a genetic algorithm to optimise timetables to suit the needs of NUS students

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering (NAACL 2021)

Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid

J.A.R.V.I.S is an AI virtual assistant made in python.

Unbiased Learning To Rank Algorithms (ULTRA)

Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI 2021)

face_recognization (FaceNet) + TFHE (HNP) + hand_face_detection (Mediapipe)

Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network"

Multi Task Vision and Language

Blender Python - Node-based multi-line text and image flowchart

SASM - simple crossplatform IDE for NASM, MASM, GAS and FASM assembly languages

Python wrapper of LSODA (solving ODEs) which can be called from within numba functions.

Pytorch domain adaptation package

Reimplementation of Learning Mesh-based Simulation With Graph Networks

Implement face detection, and age and gender classification, and emotion classification.

Code of TIP2021 Paper《SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition》. We provide both MxNet and Pytorch versions.

pytorch implementation of Attention is all you need

Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.