Advanced Scientific Machine Learning
Advanced SML lecture notes: uncertainty, inverse problems, and physics-informed learning.
Author: Ilias Bilionis (Purdue University, USA)
About this book
This online book is a curated collection of lecture notes on advanced scientific machine learning, developed as part of ME 697 at Purdue University. It targets graduate-level readers who want a probability-first view of machine learning for scientific applications, with emphasis on uncertainty, inverse problems, and physics-informed modeling.
Contents
- Modern Machine Learning Software
- Uncertainty Propagation through Scientific Models
- High-dimensional Uncertainty Propagation
- Inverse Problems in Deterministic Scientific Models
- Physics-informed Neural Networks (PINNs)
- Inverse Problems in Stochastic Scientific Models
- Homework Problems
Readership
Graduate students and researchers in engineering and science working on scientific ML applications.
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