Our mission is to develop scientific machine learning technologies to accelerate engineering innovation.
Philosophy
We operate at the intersection of mathematics, statistics, and engineering, facilitating communication between these disciplines by employing Bayesian probability and an additional layer of causality expressed through differential equations.
Information field theory as a unified paradigm for uncertainty quantification. This is where most of our efforts are at the moment.
To learn more read the papers:
(Alberts et al., 2023)(Hao et al., 2024).
Or watch this video from a recent talk at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK:
Current Projects
Bayesian hemodynamic flow reconstruction from imaging modalities using information field theory (NSF).
ME 597 - Introduction to Uncertainty Quantification. Bilionis taught this for the last time in Spring 2020. About one third of the topics taught there made it into ME 539. However, this course contains some classical uncertainty quantification lectures that Bilionis could not fit in the new course. You an find the video lectures here.
For a list of former members, please visit the PSL website.
How to join the group?
We are continuously looking for qualified people to join the group. If you are interested in working with us, please send me an email with your CV and a brief description of your research interests. I will get back to you as soon as possible.
About this website
This website was made in plain html with the help of ChatGPT.
If you want to have fun, you can see the log of our conversation here.
It was last updated on September 17, 2024.