Stanford ME 343 / CME 216 homepage

Machine Learning for Computational Engineering

These is the web site for ME 343/CME 216 Machine Learning in Computational Engineering. This material was created by Eric Darve, with the help of course staff and students.

Syllabus

Syllabus

Policy for late assignments

Extensions can be requested in advance for exceptional circumstances (e.g., travel, sickness, injury, COVID related issues) and for OAE-approved accommodations.

Submissions after the deadline and late by at most 2 days (+48 hours after the deadline) will be accepted with a 10% penalty. No submissions will be accepted 2 days after the deadline.

See Gradescope for all the current assignments and their due dates. Post on Slack if you cannot access the Gradescope class page. We will send you the 6-letter code.

Class modules and learning material

Python tutorials

Introduction to ML and SVM

Module 1-Part 1

Module 1-Part 2

Deep Learning

Module 2

Module 3-Part 1

Module 3-Part 2

Module 3-Part 3

Physics Informed Machine Learning

Module 4

Module 5

Automatic differentiation, physics informed learning using ADCME

The slides are assembled into a single PDF file. Each lecture video covers one section in the PDF.

Module 6

Inverse modeling using ADCME

Final project

Final project instructions

Reading material

Books

Video tutorials

Review papers

Online classes and tutorials