Abstract: This application relates to apparatus and methods for determining the integrity of data, such as sensor data, in systems. In some examples, a computing device receives input data for the system, and executes a physics-based model to generate a first output. The physics-based model may include a plurality of surrogate models that simulate various portions of the system. The computing device may further execute a machine learning model that operates on the first output to generate a second output. The computing device may generate a predicted output for the system based on the first output and the second output. In some examples, the computing device determines an error for the system based on the predicted output and sensor data received from sensors for the system. Based on the error, the computing device determines if the sensor data is valid. The computing device may then provide an indication of the determination.

Abstract: This application relates to apparatus and methods for generating, and executing, surrogate models. In some examples, a computing device generates and evaluates correlations between input and output variables for a system to identify highly correlated input and output parameters. In addition, weights for one or more of the parameters may be determined. The computing device identifies a mathematical relationship between input and output variables to generate a physics model. The computing device may also identify other features not captured by the physical relationship that are highly correlated to each other, and generates a feature model that is based on the highly correlated features. The computing device may optimize the feature model based on a culling process that reduces the computational resources required to execute the feature model. The physics model is then combined with the feature model to generate a system output model that can simulate the system.