ACM 154: Inverse Problems and Data Assimilation
Table of Contents
Overview
In the winter 2026 term, I co-taught ACM 154: Inverse Problems and Data Assimilation at Caltech with Prof. Andrew Stuart.
Course title: Inverse Problems and Data Assimilation
Units: 9 units (3-0-6)
Term: Second term
Description
Models in applied mathematics often have input parameters that are uncertain; observed data can be used to learn about these parameters and thereby to improve predictive capability. The purpose of the course is to describe the mathematical and algorithmic principles of this area.
The topic lies at the intersection of fields including inverse problems, differential equations, machine learning, and uncertainty quantification. Applications are drawn from the physical, biological, and data sciences.
Prerequisites
Basic differential equations, linear algebra, probability, and statistics, such as:
- ACM/IDS 104
- ACM/EE 106 ab
- ACM/EE/IDS 116
- IDS/ACM/CS 157
- Or equivalent preparation
Main Textbook
The main textbook for the course is:
Course Repositories
Related GitHub repositories:
