5 citations · 10 across the 6 of their papers we have counts for
6 papers
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method
Harbir Antil, Madhu Gupta, Randy Price
Empirical interpolation method (EIM) is a well-known technique to efficiently approximate parameterized functions. This paper proposes to use EIM algorithm to efficiently reduce th…
A stochastic Galerkin method with adaptive time-stepping for the Navier-Stokes equations
Bedřich Sousedík, Randy Price
We study the time-dependent Navier-Stokes equations in the context of stochastic finite element discretizations. Specifically, we assume that the viscosity is a random field given…
NINNs: Nudging Induced Neural Networks
Harbir Antil, Rainald Löhner, Randy Price
New algorithms called nudging induced neural networks (NINNs), to control and improve the accuracy of deep neural networks (DNNs), are introduced. The NINNs framework can be applie…
Data Assimilation with Deep Neural Nets Informed by Nudging
Harbir Antil, Rainald Löhner, Randy Price
The nudging data assimilation algorithm is a powerful tool used to forecast phenomena of interest given incomplete and noisy observations. Machine learning is becoming increasingly…
On Surrogate Learning for Linear Stability Assessment of Navier-Stokes Equations with Stochastic Viscosity
Bedřich Sousedík, Howard C. Elman, Kookjin Lee +1
We study linear stability of solutions to the Navier\textendash Stokes equations with stochastic viscosity. Specifically, we assume that the viscosity is given in the form of a~sto…
Continuous Data Assimilation for the Three Dimensional Navier-Stokes Equations
Animikh Biswas, Randy Price
In this paper, we provide conditions, \emph{based solely on the observed data}, for the global well-posedness, regularity and convergence of the Azouni-Olson-Titi data assimilation…