Modeling and Simulation of Cyber-Physical Systems
To reason with cyber-physical systems, they need to be modeled first. Modeling can be done for a variety of purposes, including diagnosis, design, explanation, configuration and control. These models can be numerical, symbolic, qualitative, teleological or statistical (e.g., neural nets). For many applications, we use a hybrid approach (a synthesis of symbolic and statistical models).
We use paradigms such as Modelica and MATLAB to construct physical models, and machine learning techniques to construct statistical models. These hybrid models are important because many of the cyber-physical systems we analyze arrive with incomplete models and incomplete data. Only through a combination of both techniques can they be modeled well enough to succeed at the task at hand. Many models can be constructed automatically or semi-automatically, thereby significantly speeding up the system modeling process. We’ve developed methods for automatically constructing models of faulty behavior from models of nominal behavior through a process we call fault augmentation. These are central to efficient and accurate condition-based maintenance.
Our work is centered around a series of Focus Areas that we believe are the future of science and technology.
We’re continually developing new technologies, many of which are available for Commercialization.
PARC scientists and staffers are active members and contributors to the science and technology communities.