activity
20182021
most citedCompressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence

58 citations · 122 across the 5 of their papers we have counts for

collaborators

5 papers

physics.plasm-ph2021

An asymptotic-preserving 2D-2P relativistic Drift-Kinetic-Equation solver for runaway electron simulations in axisymmetric tokamaks

Luis Chacon, Don Daniel, William T. Taitano

We propose an asymptotic-preserving (AP), uniformly convergent numerical scheme for the relativistic collisional Drift-Kinetic Equation (rDKE) to simulate runaway electrons in axis…

physics.flu-dyn2019★ 58 cited

Compressed Convolutional LSTM: An Efficient Deep Learning framework to Model High Fidelity 3D Turbulence

Arvind Mohan, Don Daniel, Michael Chertkov +1

High-fidelity modeling of turbulent flows is one of the major challenges in computational physics, with diverse applications in engineering, earth sciences and astrophysics, among…

physics.flu-dyn2019★ 40 cited

Optimal perturbations of gravitationally unstable, transient, boundary layers in porous media

Don Daniel, Nils Tilton, Amir Riaz

We study gravitationally unstable, transient, diffusive boundary layers in porous media using modal and nonmodal stability methods. Using nonmodal stability theory, we demonstrate…

physics.comp-ph2019★ 10 cited

A fully implicit, scalable, conservative nonlinear relativistic Fokker-Planck 0D-2P solver for runaway electron

Don Daniel, William T. Taitano, Luis Chacón

Upon application of a sufficiently strong electric field, electrons break away from thermal equilibrium and approach relativistic speeds. These highly energetic runaway electrons (…

physics.flu-dyn2018★ 14 cited

Reaction analogy based forcing for incompressible scalar turbulence

Don Daniel, Daniel Livescu, Jaiyoung Ryu

We present a novel reaction analogy (RA) based forcing method for generating stationary scalar fields in incompressible turbulence. The new method can produce more general scalar P…