6/6/22    11:00 - 13:00
Deep Learning Approaches for Applied Sciences and Engineering I  
Minisymposium organized by M. Giselle Fernández-Godino, Charles F. Jekel and Christian Gogu
Room: Jan Mayen 3
Chair: M. Giselle Fernandez-Godino
CoChair: Charles F. Jekel
Simulation of reacting flows using artificial neural networks: application to multi-regime combustion
Cédric Mehl and Damien Aubagnac-Karkar

Neural network-based filtered drag model for cohesive gas-particle flows
Josef Tausendschön, Stefan Radl and Sankaran Sundaresan

Using conservation laws to infer deep learning model accuracy of Richtmyer-Meshkov instabilities
Charles F. Jekel, Dane M. Sterbentz, Sylvie Aubry, Youngsoo Choi, Daniel A. White and Jonathan L. Belof

Deep Convolutional Autoencoders for Predicting Wind-Driven Spatial Patterns
M. Giselle Fernández-Godino, Donald D. Lucas and Qingkai Kong

Approximating the full-field temperature evolution in 3D electronic systems from randomized “Minecraft” systems
Monika Stipsitz and Helios Sanchis-Alepuz

Estimating geomechanical parameters from hydraulic fracturing tests using a soft computing-based methodology
Rafael Abreu, Cristian Mejia and Deane Roehl