Training Resources
DesignSafe-CI offers a suite of training resources aimed at equipping researchers and engineers with cutting-edge computational skills. These repositories cover a range of topics, including accelerating Python applications, utilizing database APIs, implementing physics-informed neural networks (PINNs), understanding explainable AI (XAI), and applying Deep Operator Networks (DeepONet). Each module provides hands-on tutorials and practical examples to enhance your proficiency in leveraging these technologies for natural hazards research.
DeepONet Training
Learn about Deep Operator Networks (DeepONet), a novel deep learning framework for learning operators. This training covers the fundamentals of DeepONet architecture, its applications in solving differential equations, and practical implementation using PyTorch.
Training on XAI using DesignSafe
Explore Explainable AI (XAI) techniques applied to natural hazards engineering. Learn how to interpret machine learning models using methods like LIME, SHAP, and feature importance analysis. The training includes practical examples using Python libraries and real-world datasets.
Physics Informed Machine Learning
Discover Physics-Informed Neural Networks (PINNs) for solving partial differential equations. This training demonstrates how to incorporate physical laws into neural networks, with applications in structural dynamics and fluid mechanics using TensorFlow.
Database API training
Master database interaction through APIs using DesignSafe's infrastructure. Learn to programmatically access, query, and manipulate research data using RESTful APIs. Includes examples of data retrieval, filtering, and integration with analysis workflows.
Accelerating Python using Cython, Numba and JAX
Optimize your Python code performance using various acceleration techniques. Learn to use Cython for C-level performance, Numba for just-in-time compilation, and JAX for automatic differentiation and parallelization. Includes practical examples and performance comparisons.
Note
If you don't see training that you expect to be on this list, then search for it on GitHub.