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.

Abstract: This application relates to apparatus and methods for advanced diagnostics and prognostics of systems based on physics informed machine learning processes, as well as to the training of the physics informed machine learning processes. In some examples, a processor receives system data for a system. The processor inputs the system data to a physics-based model and, based on inputting the system data to the physics-based model, generates first output data characterizing physics-based relationships of the system. Further, the processor inputs the first output data to a machine learning model and, based on inputting the first output data to the machine learning model, generates second output data. The processor determines, based on the second output data, that the machine learning model is trained. The processor stores parameters characterizing the trained machine learning model in memory.

Abstract: This application relates to apparatus and methods for electronically generating, and executing, component models for systems and system components, and determining component options for the system based on the executed component models. In some examples, a computing device generates component models for one or more components of a system. The component models may be based on features, inputs, and outputs to each system component. The computing device may execute the component models to determine one or more requirements for each component. The computing device may then search a database to determine component options that can satisfy the one or more requirements. In some examples, the computing device may display the determined component options, and may allow for the selection of one or more of the determined component options. In some examples, the computing device may allow for the purchase of the component options.

Abstract: This application relates to apparatus and methods for electronically generating, and executing, component models for systems and system components, and determining component options for the system based on the executed component models. In some examples, a computing device generates component models for one or more components of a system. The component models may be based on features, inputs, and outputs to each system component. The computing device may execute the component models to determine one or more requirements for each component. The computing device may then search a database to determine component options that can satisfy the one or more requirements. In some examples, the computing device may display the determined component options, and may allow for the selection of one or more of the determined component options. In some examples, the computing device may allow for the purchase of the component options.

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.

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.