Abstract: Systems and methods for generating reduced order models are provided herein. In embodiments, a set of learning points is identified in a parametric space. A 3D physical solver may be used to perform a simulation for each learning point in the set of learning points to generate a learning data set, where the 3D physical solver is selected from a plurality of compatible 3D physical solvers for simulating different physical aspects of a product or process. The learning data set may be compressed to reduce the learning data set to a smaller set of vectors. Coefficients from the learning data set and the smaller set of vectors may then be used to interpolate a set of coefficients within a design space for the reduced order model.

Abstract: Systems and methods for generating reduced order models are provided herein. In embodiments, a set of learning points is identified in a parametric space. A 3D physical solver may be used to perform a simulation for each learning point in the set of learning points to generate a learning data set, where the 3D physical solver is selected from a plurality of compatible 3D physical solvers for simulating different physical aspects of a product or process. The learning data set may be compressed to reduce the learning data set to a smaller set of vectors. Coefficients from the learning data set and the smaller set of vectors may then be used to interpolate a set of coefficients within a design space for the reduced order model.