Supplementary materials & lecture notes
All Juypter notebook has been tested and ran in Google Collaboratory to run the Jupyter notebooks. More information will be posted in the Blog page periodically. Please cite the corresponding references (see below) if you adopt or modify the code we provided in your research.
WCCM-APOM Short Course Lectures
Opening remarks and logistics (Sun)
Lecture 1: Graph-based learning and knowledge representation for solid mechanics ( Sun)
Lecture 2: Manifold based learning and data-driven computing for nonlinear solid mechanics: dimensional reduction and thermodynamics (Chen)
Lecture 3: Deep reinforcement learning for adversarial training of constitutive laws (Sun)
Lecture 4: Machine learning for digital twins: an example on musculoskeletal digital twin (Chen)
Lecture 1: Graph-based learning and knowledge representation for solid mechanics ( Sun)
Lecture 2: Manifold based learning and data-driven computing for nonlinear solid mechanics: dimensional reduction and thermodynamics (Chen)
Lecture 3: Deep reinforcement learning for adversarial training of constitutive laws (Sun)
Lecture 4: Machine learning for digital twins: an example on musculoskeletal digital twin (Chen)
WCCM-APOM Short Course Jupyter Notebook
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Topics
Tutorial 1: Introduction to Artificial Neural Networks
Tutorial 2: Setup machine Learning Environment Tutorial 3: Artificial neural network for predicting of failure envelope of Carbon/Epoxy Composites Tutorial 4: Design of experiments with deep reinforcement learning |
Materials shared via Google Drive
Jupyter notebook
Jupyter notebook Jupyter notebook Jupyter notebook (Please request access for this tutorial) |
Video
Videos will be posted after the live short course
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