Shawn Shadden
Tien-Modak Chancellor’s Chair
University of California Berkeley
Seminar Information
Engineering Building Unit 2 (EBU2)
Room 479
Seminar Recording Available: Please contact seminar coordinator, Jake Blair at (j1blair@ucsd.edu)
We present a method for reconstructing flow fields from noisy, low-resolution measurements using a combination of Bayesian inference and the finite-element method. We refer to this approach as finite element regression. The method conditions a physics-informed prior on flow measurements to obtain a posterior distribution whose maximum a posteriori estimate provides velocity and pressure reconstructions and, under appropriate assumptions, enables uncertainty quantification of reconstructed quantities. We focus on applications to vascular flow and compare finite element regression with other reconstruction approaches, including a physics-informed neural network (PINN).
Shawn Shadden holds the Tien-Modak Chancellor's Chair in Mechanical Engineering at the University of California, Berkeley, where he is also a core member of the UCSF-UC Berkeley Graduate Program in Bioengineering. His research focuses on the computational modeling of cardiovascular biomechanics and the advancement of theoretical and numerical methods to quantify complex fluid flow. He is recipient of an NSF CAREER Award, a Bakar Faculty Fellow Award, Hellman Faculty Fellow Award, and the American Heart Association’s Established Investigator Award. His lab helps develop the SimVascular software platform, which is broadly used in the field of computational cardiovascular research, as well as other community software tools.