69 citations · 94 across the 7 of their papers we have counts for
8 papers
Training of particle-turbulence sub-grid-scale closures with just particle data
G. Saltar Rivera, L. Villafane, J. B. Freund
If sufficient training data are available, neural networks are attractive for representing missing physics in simulations, such as sub-grid scales in the coarse-mesh particle-turbu…
Object Transport by a Confined Active Suspension
Jonathan B. Freund
Numerical simulations in two space dimensions are used to examine the dynamics, transport, and equilibrium behaviors of a neutrally buoyant circular object immersed in an active su…
Non-equilibrium plasma generation via nano-second multi-mode laser pulses
Andrea Alberti, Alessandro Munafó, Munetake Nishihara +3
A non-equilibrium model for laser-induced plasmas is used to describe how nano-second temporal mode-beating affects plasma kernel formation and growth in quiescent air. The chemica…
Embedded training of neural-network sub-grid-scale turbulence models
Jonathan F. MacArt, Justin Sirignano, Jonathan B. Freund
The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses in a temporally developing…
DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)
Jonathan B. Freund, Jonathan F. MacArt, Justin Sirignano
Machine learning for scientific applications faces the challenge of limited data. We propose a framework that leverages a priori known physics to reduce overfitting when training o…
A mechanism for damage spread by ultrasound-induced linear defects in soft materials
Pooya Movahed, Jonathan B. Freund
High-intensity ultrasound excites pre-existing bubbles in tissue-like material, and the subsequent bubble activity may lead to damage. To investigate such damage mechanisms, agar t…