A computer-implemented method that enables the selection of materials for industrial assets is described. The method includes obtaining historical data from a database. Industry standards are integrated with this historical data, and the data is filtered to create multiple training datasets that comply with these standards. The method further involves training one or more machine learning models using these datasets. A recommendation for material selection is generated based on the predictions from the trained models, using a validation mechanism to ensure compliance with industry standards.

An acquisition unit (11) acquires an explanatory variable that is to be input to a model (37) configured to output an objective variable for the explanatory variable, a specification unit (12) associates a frequency at which an explanatory variable included in each of a plurality of areas, which are obtained by dividing an explanatory variable space, is acquired by the acquisition unit (11) with each of the plurality of areas, and specifies an area to which an explanatory variable included in learning data used to learn the model (37) belongs and in which a frequency of an explanatory variable acquired by the acquisition unit (11) is a predetermined value or less, and an update unit (14) updates the model (37) in such a manner that learning data including an explanatory variable belonging to an area specified by the specification unit (12) is forgotten.