Award Data

Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

The objective of this project is to develop a capability to model fracture of materials used in hypersonic vehicles that results from hypervelocity impact while exposed to extreme temperatures. Symplectic Engineering’s approach addresses this challenge at two levels. At the computational level, a Relaxed Extended Finite Element approach is pursued to represent (possibly intersecting) fractures l ...

This STTR research study aims to enhance and apply thermo-mechanically coupled computational models for high-temperature fracture. This topic is particularly challenging in that hypersonic flight in the atmosphere generates extreme conditions over a vehicle that can affect the strength and performance the vehicle materials, both in-flight conditions as well as for cases where the vehicle encounter ...

Current methods of validating the cyber resiliency of missile defense systems require testing of actual systems, which removes them from operation and subjects them to potentially damaging effects. Cyber ranges can be used as an alternative, but they are limited in scale, costly, time-consuming to configure and have limited capability to model wireless tactical networks and their inherent vulnerab ...

The proposed work will develop a massively parallelized architecture for Monte Carlo simulation of real-time fire control schedulers. The parallelized architecture will utilize the recent developments in multiprocessing/multithreading technology based on graphical processing units. The system will be designed to perform in real-time assuming non-collocated sensors observing the threats and then co ...