Abstract: A method, apparatus, system, and computer program product for estimating material properties. Training data comprising results of testing samples for a set of materials over a range of loads applied to the samples is identified by a computer system. A machine learning model is trained by the computer system to output the material properties for materials in structures using the training data.

Abstract: A method, apparatus, system, and computer program products for managing a set of outliers in test data. A computer system analyzes a set of features derived from the test data using different outlier detection methods to generate a result of the set of outliers identified by the different outlier detection methods. The test data is obtained from testing a physical structure. The computer system determines a causality for the set of outliers in the result. The physical structure is retested with a set of changes determined using the causality identified for the set of outliers. The retesting generates new test data for the physical structure.

Abstract: A method, apparatus, system, and computer program product for managing a physics simulation model. A machine learning model is trained to output predicted test results for sets of simulation values for a set of simulation parameters using a training data set based on test results for physical structures to form a surrogate model. Current simulation values for simulation parameters are selected using the surrogate model and a cost function. Simulation test results are generated using the physics simulation model that implements the current simulation values selected for the simulation parameters. The simulation test results are compared with physical test results from testing the set of physical structures using physical test inputs applied to the physical structures to form a comparison. The surrogate model is trained using the current simulation values selected for the simulation parameters using the surrogate model in response to the comparison being outside of a tolerance.

Abstract: A method of assessing performance of a seat is provided. The method comprises identifying a number of critical seats for testing from a layout of passenger accommodation and building a computer simulation model, following a building block approach, for each identified critical seat. A number of loads are tested on each simulation model. Critical seats are selected for physical testing from simulation model test results according to a specified criteria assessment matrix.

Abstract: A method, apparatus, system, and computer program product for estimating material properties. Training data comprising results of testing samples for a set of materials over a range of loads applied to the samples is identified by a computer system. A machine learning model is trained by the computer system to output the material properties for materials in structures using the training data.

Abstract: A method of assessing performance of a seat is provided. The method comprises identifying a number of critical seats for testing from a layout of passenger accommodation and building a computer simulation model, following a building block approach, for each identified critical seat. A number of loads are tested on each simulation model. Critical seats are selected for physical testing from simulation model test results according to a specified criteria assessment matrix.