Gaussian Process Hydrodynamics (3d, 2 modes)
0:41
Gaussian Process Hydrodynamics (3d, 2 modes)
Gaussian Process Hydrodynamics (3d, 1 mode)
0:41
Gaussian Process Hydrodynamics (3d, 1 mode)
Velocity animation (long, with a Gaussian kernel characterized by only one scale/mode)
1:07
Velocity animation (long, with a Gaussian kernel characterized by only one scale/mode)
Vorticity animation (shear flow, Gaussian Process Hydrodynamics)
0:17
Vorticity animation (shear flow, Gaussian Process Hydrodynamics)
Velocity Animation (shear flow, Gaussian Process Hydrodynamics)
0:17
Velocity Animation (shear flow, Gaussian Process Hydrodynamics)
Velocity animation around an obstacle (stick boundary condition, Gaussian Process Hydrodynamics)
0:14
Velocity animation around an obstacle (stick boundary condition, Gaussian Process Hydrodynamics)
Residual animation (Gaussian Process Hydrodynamics)
0:14
Residual animation (Gaussian Process Hydrodynamics)
Variance vorticity animation (Gaussian Process Hydrodynamics)
0:14
Variance vorticity animation (Gaussian Process Hydrodynamics)
Variance velocity animation (Gaussian Process Hydrodynamics)
0:14
Variance velocity animation (Gaussian Process Hydrodynamics)
Vorticity animation (Gaussian Process Hydrodynamics)
0:14
Vorticity animation (Gaussian Process Hydrodynamics)
Velocity Animation (Gaussian Process Hydrodynamics)
0:14
Velocity Animation (Gaussian Process Hydrodynamics)
On solving/learning differential equations with kernels. Houman Owhadi. FAU seminar. Oct 2021.
40:45
On solving/learning differential equations with kernels. Houman Owhadi. FAU seminar. Oct 2021.
Houman Owhadi: On learning adapted kernels. One World Numerical Analysis Series.
48:07
Houman Owhadi: On learning adapted kernels. One World Numerical Analysis Series.
SIAM CSE 2015 Plenary talk. Solving PDEs as a machine learning/inference/UQ problem. Houman Owhadi
43:16
SIAM CSE 2015 Plenary talk. Solving PDEs as a machine learning/inference/UQ problem. Houman Owhadi
Do ideas have shape? Plato’s theory of forms as the continuous limit of artificial neural networks.
58:47
Do ideas have shape? Plato’s theory of forms as the continuous limit of artificial neural networks.
Learning patters with kernels and learning kernels from patterns (PhD defense). Gene Ryan Yoo.
51:59
Learning patters with kernels and learning kernels from patterns (PhD defense). Gene Ryan Yoo.
Sparse Cholesky factorization by Kullback-Leibler minimization (Florian T. Schäfer)
52:00
Sparse Cholesky factorization by Kullback-Leibler minimization (Florian T. Schäfer)
Kernel Flows
0:21
Kernel Flows
Kernel Flows (time averaged flow)
0:21
Kernel Flows (time averaged flow)
Kernel Flows.
0:21
Kernel Flows.
Swis Roll Cheesecake classification problem
0:17
Swis Roll Cheesecake classification problem
Kernel Flows
0:04
Kernel Flows
Kernel Flows
0:03
Kernel Flows
Kernel Flows
0:05
Kernel Flows
Kernel Flows
0:17
Kernel Flows
Kernel Flows
0:17
Kernel Flows
Kernel Flows
0:05
Kernel Flows
Kernel Flows
0:18
Kernel Flows
Kernel Flows
0:17
Kernel Flows
Wave equation, high conductivity channel, diff between fine mesh and reduced basis sol (with buffer)
0:07
Wave equation, high conductivity channel, diff between fine mesh and reduced basis sol (with buffer)