UNIVERSITY OF TEXAS AT AUSTIN PROPOSE TO CREATE EFFICIENT, ACCURATE, AND SCALABLE DEEP NEURAL NETWORK (DNN) REPRESENTATIONS OF SOLUTION OF DESIGN OPTIMIZATION PROBLEMS. THE INPUTS REPRESENT THE VECTOR OF DESIGN REQUIREMENT PARAMETERS, THE OUTPUTS REPRESENT THE OPTIMAL DESIGN VARIABLES, AND THE GOAL IS TO LEARN THE MAP FROM INPUTS TO OUTPUTS, I.E. THE INVERSE DESIGN MAP.
$1,615,866FY2021Department of EnergyDOE
University Of Texas At Austin, Austin TX