Machine learning algorithms have demonstrated state-of-the-art automated target recognition performance but require a large training set. In the case of electro-optical infrared (EO/IR) remote sensing, acquiring sufficient measured imagery can be difficult, but EO/IR scene simulation is a possible alternative. CoTherm, a co-simulation tool which operates MuSES in an automated fashion, is used to manipulate relevant target, background and sensor inputs to generate a library of radiance images. Various options affecting simulation run-time and output fidelity are considered and the trade-off between accuracy and compute time requirements is quantified using a measured imagery benchmark and ResNets for classification.

Following Matt Young’s talk will be several short talks:

https://appliedmath.arizona.edu/events/raytheon-day-fall-2023-symposium

Speaker: Eric Anderson (RTX, Senior Principal Engineer)
Title:  Covariances and the Eigenvalue Decomposition
Time: 3:40-4:20

Speaker:  Theodore Meissner, Program in Applied Mathematics, University of Arizona
Title:  Sparse Optimization Techniques for Differential Equation Discovery
Time:  4:25-4:55

Speaker: Patrick Kano (RTX, Principal Engineer) (Alumni – Program in Applied Mathematics)
Title: Dempster-Shafer Evidential Theory for Optimal Contours in Talbot’s Method
Time: 5:00-5:40

5:40-5:50  Break

Speaker: Nitesh Shah (RTX, Senior Principal Engineer)
Title: Introduction to RTX Processing, Guidance, and Control CenterTime: 5:50-6:15

Speakers: Lexi Casarez (RTX, Campus Programs Recruiter), RTX Staff and UAZ Faculty
Title:  Question and Answer Session

Speaker:          Michael (Misha) Chertkov, Professor and Chair, Program in Applied Mathematics, University of Arizona
Title:               Closing