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An Army specialist overhauls a generator. Photo by Sgt. Margaret Taylor, courtesy of U.S. Army

US Army to Leverage Charles River Analytics’ Figaro™ Technology to Predict Faults in Military Operations Systems

Charles River Analytics Inc., developer of intelligent systems solutions, has announced two contracts for the US Army, as part of continuing research applying autonomous reasoning to improve mission performance. The Army will leverage Charles River’s Figaro™ technology under these efforts to help predict faults in military operations systems. Figaro is Charles River’s open-source, probabilistic programming language for probabilistic modeling.

The Army is seeking effective management of energy and supply resources that are needed to train, move, and sustain forces and systems in military operations. Under the Energy Models of Critical Components effort, or E-MC2, Charles River is designing a resource monitoring, energy prediction, and decision-support tool that uses probabilistic reasoning to construct and learn models of complex, real-world systems. Using E-MC2’s models, commanders will receive notifications on possible resource consumption issues.

To avoid power failures in critical situations, the Army also seeks modeling tools that provide prognostics and diagnostics for backup power equipment. Under the Probabilistic Operations Warranted for Energy Reliability Evaluation and Diagnostics, or POWERED, effort, Charles River is using rich, modular probabilistic modeling to report on the reliability of back-up generators.

“We’re using the flexibility of Figaro in these two efforts,” said Dr. Avi Pfeffer, Chief Scientist at Charles River and developer of Figaro. “Figaro lets us quickly assemble models of interacting components that use a combination of physics-based, heuristic, and data-driven reasoning. In E-MC2, we are using Figaro to build models of energy consumption and resource usage. In POWERED, Figaro will provide detailed models of power generator health and status.”

Visit the Figaro page to learn more.


This material is based upon work supported by the CERDEC Command, Power and Integration Directorate (CP&I) under contract number: W56KGU-16-C-0050, and the Engineering Research and Development Center – Construction Engineering Research Laboratory (ERDC-CERL) under Contract No. W9132T-16-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of CERDEC Command, Power and Integration Directorate (CP&I) nor the ERDC-CERL.

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