DDPS | Approximating functions, functionals, and operators using deep neural networks

(1:15:29)

DDPS | CUR Matrix Decomposition for Scalable Reduced-Order Modeling

(59:24)

DDPS | A mathematical understanding of modern Machine Learning: theory, algorithms and applications

(1:12:)

DDPS | “Infinite Dimensional Optimization for Scientific Machine Learning”

(50:55)

DDPS | Model reduction of partial differential equations via optimization-based feature tracking

(1:7:52)

DDPS | Deep neural operators with reliable extrapolation for multiphysics \u0026 multiscale problems

(59:40)

DDPS|Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models

(57:39)

DDPS | “A first-principles approach to understanding deep learning”

(1:17:33)

DDPS | Data-driven information geometry approach to stochastic model reduction

(57:56)

DeepOnet: Learning nonlinear operators based on the universal approximation theorem of operators.

(58:12)

DDPS | ‘Elliptic PDE learning is provably data-efficient’

(59:3)

DDPS | “Machine-Precision Neural Networks for Multiscale Dynamics”

(1:8:41)

On Approximating the Covering Radius and Finding Dense Lattice Subspaces

(31:42)

DDPS | Trustworthy learning of mechanical systems \u0026 Stiefel optimization with applications

(1:7:26)

DDPS | Learning paradigms for neural networks: The locally backpropagated forward-forward algorithm

(56:40)

DDPS | Data-driven modeling of dynamical systems: A systems theoretic perspective

(56:14)

DDPS | libROM: Library for physics-constrained data-driven physical simulations | Youngsoo Choi

(1:7:31)

DDPS | Towards reliable, efficient, and automated model reduction of parametrized nonlinear PDEs

(1:4:6)

DDPS | Data-assisted Algorithms for Inverse Random Source Scattering Problems by Ying Liang

(52:46)