5 citations · 10 across the 8 of their papers we have counts for
5 papers · 1 filter
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
Guillermo Barragán, Ashton Hetherington, Arindam Sengupta +3
We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The meth…
Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics
Paul Jeanney, Ashton Hetherington, Shady E. Ahmed +4
This paper presents an innovative Reduced-Order Model (ROM) for merging experimental and simulation data using Data Assimilation (DA) to estimate the "True" state of a fluid dynami…
HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics
Guillermo Barragán, Ashton Hetherington, Rodrigo Abadía-Heredia +2
In this work we present a novel methodology that combines Higher Order Singular Value Decomposition (HOSVD) with Deep Learning (DL) techniques for super-resolution in computational…
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models
Arindam Sengupta, Rodrigo Abadía-Heredia, Ashton Hetherington +2
Accurate modeling of the complex dynamics of fluid flows is a fundamental challenge in computational physics and engineering. This study presents an innovative integration of High-…
A low cost singular value decomposition based data assimilation technique for analysis of heterogeneous combustion data
Prajith Pillai, Ashton Hetherington, Laura Saavedra Sago +1
This article applies low-cost singular value decomposition (lcSVD) for the first time, to the authors knowledge, on combustion reactive flow databases. The lcSVD algorithm is a nov…