Applications of Data Analytics to DoD Challenges

Artificial Intelligence (AI) is the science of making intelligent machines, especially intelligent computer programs. Machine learning (ML) is a branch of artificial intelligence which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Neural networks are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within research, development, testing, and evaluation. Weapons Systems & Platforms is all about making more environmentally friendly war-fighting products. Munitions Response is all about the detection, classification, and remediation of unexploded ordnance. Both program areas research and development are becoming more involving with Big Data and the requirements to explore the applications AI, ML, and the different types of Neural Networks. 

Session Chair: Mr. Charles Serafini, USAEC
Introduction by Session Chair Mr. Charles Serafini, U.S. Army Environmental Command
Using Machine Learning to Integrate Historical and Real-Time Data into a Predictive Algorithm Dr. Christine Sanders, U.S. Naval Research Laboratory
Machine Learning for DoD Use Cases: Why Is It Hard and What Can We Do About It? Dr. Shelley Cazares, Institute for Defense Analyses
Physics-Informed Machine Learning: Accelerating Materials Design Dr. Alejandro Strachan, Purdue University
Using Machine Learning Techniques to Estimate Initial Model Parameters Dr. Steven Policastro, U.S. Naval Research Laboratory
Application of Machine Learning to Environment Severity, Corrosivity, and Coating Performance Dr. Fritz Friederdorf, Luna

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