Machine Learning for Physics and the Physics of Learning Tutorials

September 5 - 10, 2019

Schedule

All times in this Schedule are Pacific Time (PT)

Thursday, September 5, 2019

Morning Session

8:00 - 8:55 Check-In/Breakfast (Hosted by IPAM)
8:55 - 9:00 Welcome & Opening Remarks (Dima Shlyakhtenko)
9:00 - 10:15
Frank Noe (Freie Universität Berlin)

Intro to Machine Learning part 1
PDF Presentation

 
10:15 - 10:45 Break
10:45 - 12:00
Steve Brunton (University of Washington)

Dynamical Systems part 1
PDF Presentation

 
12:00 - 12:30 Core Orientation
12:30 - 2:00 Lunch (on your own)

Afternoon Session

2:00 - 3:15
Tristan Bereau (Max Planck Institute for Polymer Research)

Constructing Conservation Laws with Machine Learning for Energy Landscapes
PDF Presentation

 
3:15 - 3:45 Break
3:45 - 5:00
Anatole von Lilienfeld (University of Basel)

Introduction to Electronic Structure Calculations

 

Friday, September 6, 2019

Morning Session

8:00 - 9:00 Check-In/Breakfast (Hosted by IPAM)
9:00 - 10:15
Frank Noe (Freie Universität Berlin)

Intro to Machine Learning part 2
PDF Presentation

 
10:15 - 10:45 Break
10:45 - 12:00
Steve Brunton (University of Washington)

Dynamical Systems part 2
PDF Presentation

 
12:00 - 2:00 Lunch (on your own)

Afternoon Session

2:00 - 3:15
Marina Meila (University of Washington)

Manifold Learning
PDF Presentation

 
3:15 - 3:45 Break
3:45 - 5:00
Gianni De Fabritiis (Universitat Pompeu Fabra)

Machine Learning for Drug Design

 

Monday, September 9, 2019

Morning Session

8:00 - 9:00 Check-In/Breakfast (Hosted by IPAM)
9:00 - 10:15
Patrick Riley (Google)

Data preparation and feature engineering

10:15 - 10:45 Break
10:45 - 12:00
12:00 - 2:00 Lunch (on your own)

Afternoon Session

2:00 - 3:15
3:15 - 3:45 Break
3:45 - 5:00
Kyle Cranmer (New York University)

The interplay between physically motivated simulations and machine learning
PDF Presentation

 

Tuesday, September 10, 2019

Morning Session

8:00 - 9:00 Check-In/Breakfast (Hosted by IPAM)
9:00 - 10:15
Patrick Riley (Google)

Graph based neural networks for prediction and generation

10:15 - 10:45 Break
10:45 - 12:00
12:00 - 2:00 Lunch (on your own)

Afternoon Session

2:00 - 3:15
Frank Noe (Freie Universität Berlin)

Connection between Statistics and Machine Learning
PDF Presentation

 
3:15 - 3:45 Break
3:45 - 5:00
Steve Brunton (University of Washington)

Introduction to Fluid Mechanics
PDF Presentation