Schedule for: 23w5129 - Scientific Machine Learning
Beginning on Sunday, June 18 and ending Friday June 23, 2023
All times in Banff, Alberta time, MDT (UTC-6).
Sunday, June 18 | |
---|---|
16:00 - 17:30 | Check-in begins at 16:00 on Sunday and is open 24 hours (Front Desk - Professional Development Centre) |
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) |
20:00 - 22:00 |
Informal gathering ↓ Meet and Greet Informal Meeting at BIRS Lounge in PDC second floor (Other (See Description)) |
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) |
Tuesday, June 20 | |
---|---|
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 - 09:30 | Yunan Yang: Neural Inverse Operators for Solving PDE Inverse Problems (TCPL 201) |
09:30 - 10:00 | Aras Bacho (TCPL 201) |
10:00 - 10:30 | Coffee Break (TCPL Foyer) |
10:30 - 11:00 | Eric Cyr: Exploiting time-domain parallelism to accelerate neural network training (TCPL 201) |
11:00 - 11:30 | Paolo Zunino: A Deep Learning approach to Reduced Order Modelling of Parameter dependent Partial Differential Equations (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 - 13:45 | Robert Scheichl: Low-Rank Tensor Based Surrogates for Scientific Machine Learning (TCPL 201) |
13:45 - 14:15 | Benjamin Sanderse: Structure-preserving learning of embedded closure models for fluid flows (TCPL 201) |
14:15 - 14:45 |
Coffee Break ↓ Coffee Break in TCPL Foyer (TCPL Foyer) |
14:45 - 15:15 | N. Sukumar: Exact Imposition of Boundary Conditions in PINNs to Solve PDEs (TCPL 201) |
15:15 - 15:45 | Mihai Nica (TCPL 201) |
15:45 - 16:15 | Deepanshu Verma: Advances and challenges in solving high-dimensional HJB equations (Online) |
16:15 - 17:30 | Scheduled time for collaborative research discussions (TCPL Lounge) |
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) |
Wednesday, June 21 | |
---|---|
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) |
09:00 - 09:30 | Jakob Zech: Nonparametric Distribution Learning via Neural ODEs (TCPL 201) |
09:30 - 10:00 | Nicholas Nelsen: Convergence Theory for Vector-Valued Random Features (TCPL 201) |
10:00 - 10:30 | Coffee Break (TCPL Foyer) |
10:30 - 11:00 | Margaret Trautner: Learning Homogenized Constitutive Laws (TCPL 201) |
11:00 - 11:30 | Guang Lin: Energy-Dissipative Evolutionary Deep Operator Neural Networks (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 - 13:30 | Tom O'Leary-Roseberry: Derivative-Informed Neural Operators for High-Dimensional Outer-Loop Problems (TCPL 201) |
13:30 - 14:00 | Jinwoo Go: Accelerating A-Optimal/D-Optimal Design of Experiments Using Neural Networks (TCPL 201) |
14:00 - 14:30 | Dingcheng Luo: Efficient PDE-constrained optimization with derivative-informed neural operators (TCPL 201) |
14:30 - 15:00 | Coffee break (TCPL Foyer) |
15:00 - 15:30 | Bruno Despres: Generating functions for polynomials with ReLU: application to training (TCPL 201) |
15:30 - 16:00 | Marta D'Elia: GNN-based physics solver for time-independent PDEs (Online) |
16:00 - 16:30 | Shunyuan Mao: PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems (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) |
Thursday, June 22 | |
---|---|
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) |
09:00 - 10:00 | Petros Koumoutsakos: AI /Scientific Computing: Alloys for Flow modeling and Control (Online) |
10:00 - 10:30 | Coffee Break (TCPL Foyer) |
10:30 - 11:00 | Peng Chen: Projected variational inference for high-dimensional Bayesian inverse problems. (TCPL 201) |
11:00 - 11:30 | Lu Lu: Deep neural operators with reliable extrapolation for multiphysics, multiscale & multifidelity problems (Online) |
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 - 17:30 | Free afternoon (Banff National Park) |
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) |
Friday, June 23 | |
---|---|
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 - 10:00 | Scheduled time for collaborative research discussions (TCPL Lounge) |
10:00 - 10:30 | Coffee Break (TCPL Foyer) |
10:30 - 11:00 |
Checkout by 11AM ↓ 5-day workshop participants are welcome to use BIRS facilities (TCPL ) until 3 pm on Friday, although participants are still required to checkout of the guest rooms by 11AM. (Front Desk - Professional Development Centre) |
12:00 - 13:30 | Lunch from 11:30 to 13:30 (Vistas Dining Room) |