Monday July 6
13.00 - 14.00
Registration
14.00 - 15.30
Lecture 1: Gaussian Process (GP) Modeling
15.30 - 16.00
Coffee break
16.00 - 17.30
Lecture 2: GP Design
17.30 - 18.30
Practicum 1: GP Modeling & Design
Tuesday July 7
9.00 - 10.30
Lecture 3: Boundary-Informed Bayesian Modeling
10.30 - 11.00
Coffee break
11.00 - 12.30
Practicum 2: Boundary-Informed Bayesian Modeling
14.00 - 15.30
Lecture 4: Shape-Constrained Bayesian Modeling
15.30 - 16.00
Coffee break
16.00 - 18.00
Participants' session
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Bayesian Optimization in Inverse Problems Mike Baumann, Simone G¨ttlich, Simon Weissmann
Bayesian Hierarchical Modeling for Reliable Large-Scale Sensors Deployments and Applications Anna Ballario
Surrogate Multi-Fidelity Modeling for Financial Options Pricing Using Gaussian Processes Mirko Piazzalunga, Tommaso Moretti
Sampling-based Batch Sequential Design by Stein Variational Gradient Descent Xiaoxian Ding, Penghui Fu, C. F. Jeff Wu
Data Efficient Optimization of Catalytic Reactions Using Bayesian Optimization Ertugrul Furkan Düzenli
Bayesian machine learning for analog computation Matteo Spaziani
Wednesday July 8
9.00 - 10.30
Practicum 3: Shape-Constrained Bayesian Modeling
10.30 - 11.00
Coffee break
11.00 - 12.00
Lecture 5: Multi-Fidelity Bayesian Learning
12.00 - 13.00
Practicum 4: Multi-Fidelity Bayesian Learning
Thursday July 9
9.00 - 10.30
Lecture 6: Physics-Informed Learning
10.30 - 11.00
Coffee break
11.00 - 12.30
Practicum 5: Physics-Informed Learning
14.00 - 15.30
Lecture 7: Data-Driven PDE Discovery
15.30 - 16.00
Coffee break
16.00 - 17.30
Practicum 6: Data-Driven PDE Discovery
Friday July 10
9.00 - 10.30
Lecture 8: Bayesian Active Learning & Optimization
10.30 - 11.00
Coffee break
11.00 - 12.30
Practicum 8: Lab - Bayesian Active Learning & Optimization
IMPORTANT NOTE:
It is important to have your own PC for the practical lessons. Remember to take it with you before
leaving. Please install the following software on your PC in advance to start your lessons smoothly:
Prof. Mak will invite all participants to a Slack channel for the summer school, where participants can ask questions, get interactive feedback and find the lecture and lab materials.
REFERENCES:
We will begin with the following textbook, then explore recent papers from the literature.
Gramacy, R. B. (2020). Surrogates: Gaussian process modeling, design, and optimization for the applied sciences. Chapman and Hall/CRC
All participants will receive a certificate of attendance at the end of the course.
The university / ECTS credits granted for attendance of the course are established by the Director of your specific course of study,
depending on your university/course criteria
