| Monday, June 19 |
| 07:00 - 08:45 |
Breakfast ↓ Breakfast is served daily between 7 and 9am in the Vistas Dining Room, the top floor of the Sally Borden Building. (Vistas Dining Room) |
| 08:45 - 09:00 |
Introduction and Welcome by BIRS Staff ↓ A brief introduction to BIRS with important logistical information, technology instruction, and opportunity for participants to ask questions. (TCPL 201) |
| 09:00 - 10:00 |
Panos Stinis: Multifidelity Scientific Machine Learning ↓ In many applications across science and engineering it is common to have access to disparate types of data or models with different levels of fidelity. In general, low-fidelity data are easier to obtain in greater quantities, but it may be too inaccurate or not dense enough to accurately train a machine learning model. High-fidelity data is costly to obtain, so there may not be sufficient data to use in training, however, it is more accurate. A small amount of high-fidelity data, such as from measurements or simulations, combined with low fidelity data, can improve predictions when used together. The important step in such constructions is the representation of the correlations between the low- and high-fidelity data. In this talk, we will present two frameworks for multifidelity machine learning. The first one puts particular emphasis on operator learning, building on the Deep Operator Network (DeepONet). The second one is inspired by the concept of model reduction. We will present the main constructions along with applications to closure for multiscale systems and continual learning. Moreover, we will discuss how multifidelity approaches fit in a broader framework which includes ideas from deep learning, stochastic processes, numerical methods, computability theory and renormalization of complex systems. (TCPL 201) |
| 10:00 - 10:30 |
Coffee Break (TCPL Foyer) |
| 10:30 - 11:00 |
Romit Maulik (TCPL 201) |
| 11:00 - 11:30 |
Scott Field: Potential Applications of Scientific Machine Learning to the Binary Black Hole Problem ↓ One of the most important astrophysical applications of general relativity is solving the binary black hole (two-body) problem. This is stated as a parameterized initial-boundary-value problem: the initial data describes two black holes parameterized by their mass, spin, and initial velocities. A key output from the code is the emitted gravitational wave signal. This waveform signal, which can now be measured by gravitational-wave detectors, encodes information about the black holes' dynamics as well as wave propagation effects. Decades of effort have been directed toward building gravitational wave models and dynamical systems describing black hole motion. In this talk, I will summarize the modeling problem as well as brainstorm some areas that could benefit from scientific machine learning. (TCPL 201) |
| 11:30 - 13:00 |
Lunch ↓ Lunch is served daily between 11:30am and 1:30pm in the Vistas Dining Room, the top floor of the Sally Borden Building. (Vistas Dining Room) |
| 13:00 - 14:00 |
Guided Tour of The Banff Centre ↓ Meet in the PDC front desk for a guided tour of The Banff Centre campus. (PDC Front Desk) |
| 14:00 - 14:20 |
Group Photo ↓ Meet in foyer of TCPL to participate in the BIRS group photo. The photograph will be taken outdoors, so dress appropriately for the weather. Please don't be late, or you might not be in the official group photo! (TCPL Foyer) |
| 14:20 - 15:00 |
Bart van Bloemen Waanders: Learning control policies for high-fidelity models using hyper-differential sensitivities with respect to model-discrepancy (TCPL 201) |
| 15:00 - 15:30 |
Coffee Break (TCPL Foyer) |
| 15:30 - 16:30 |
Animashree Anandkumar (Online) |
| 16:30 - 17:00 |
Michael Brennan: Exploiting Low-Rank Conditional Structure to Solve Bayesian Inverse Problems (TCPL 201) |
| 17:30 - 19:30 |
Dinner ↓ A buffet dinner is served daily between 5:30pm and 7:30pm in Vistas Dining Room, top floor of the Sally Borden Building. (Vistas Dining Room) |
