Charles River Analytics Inc., developer of intelligent systems solutions, has developed a suite of probabilistic model-based programming techniques for prediction, analysis, and control—ProbSysML. ProbSysML provides probabilistic extensions for model-based systems engineering frameworks, such as the Systems Modeling Language (SysML). With ProbSysML suite’s accurate analyses and predictions of system performance under uncertainty, system modelers can create better designs faster.
“We’re helping engineers create system models they can use to control and reason about the operating behavior of systems in uncertain environments,” said Dr. Avi Pfeffer, Chief Scientist at Charles River Analytics and Principal Investigator on the ProbSysML effort. “ProbSysML provides probabilistic extensions to the standard Systems Modeling Language and compiles to our Figaro™ probabilistic programming language.”
ProbSysML provides inference services built on our open-source Figaro probabilistic programming language to support system performance prediction and design analysis in the face of uncertainty.
Read more about ProbSysML.
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) and the Army Contracting Command-Aberdeen Proving Grounds (ACC-APG) under Contract No. W911NF-15-C-0002. 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 the Defense Advanced Research Projects Agency (DARPA) and the Army Contracting Command-Aberdeen Proving Grounds (ACC-APG).