US 12,229,934 B1
Method and system for training an artificial neural network utilizing physics based knowledge
James Derek Tucker, Edgewood, NM (US); and Matthew Thomas Martinez, Albuquerque, NM (US)
Assigned to National Technology & Engineering Solutions of Sandia, LLC, Albuquerque, NM (US)
Filed by National Technology & Engineering Solutions of Sandia, LLC, Albuquerque, NM (US)
Filed on Jan. 19, 2022, as Appl. No. 17/579,324.
Int. Cl. G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06V 10/82 (2022.01)
CPC G06T 7/0004 (2013.01) [G06V 10/82 (2022.01); G06T 2200/24 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
10. A system for training a classifier for a material characterization comprising:
at least one processor; a non-transitory, computer-readable medium having instructions stored thereon that are executable by the at least one processor to cause the system to:
obtain the functional data having phase and amplitude;
register functional data by phase-amplitude separation of the functional data to produce separated phase and amplitude components with an elastic distance and perform statistical analysis on the separated phase and amplitude components to produce aligned functional data;
perform dimensional reduction on the aligned functional data to produce a dimensional representation of a functional space of the aligned functional data; and
train a classifier with the dimensional representation of the functional space.