The invention discloses a multi-fidelity
matrix completion method based on a
generative adversarial network, and belongs to the technical field of data fusion. The method comprises the following steps: firstly, performing matrix data enhancement on a complete low-fidelity matrix and a high-fidelity matrix containing missing items to obtain a
training set; secondly, selecting training data of which the
batch number is B from the
training set, fixing the weight of a generator, and updating the weight of a
discriminator; selecting new training data of which the
batch number is B from the
training set, fixing the weight of the generator, and updating the weight of the generator; and in a certain round T, traversing the training set, and minimizing discrimination loss and
generation loss through the above weight cross updating method. And finally, inputting the complete low-fidelity matrix and the high-fidelity matrix containing the missing item into the
generative adversarial network after weight updating to obtain a complete high-fidelity matrix. According to the method, the missing items in the high-fidelity matrix are complemented by fusing the available items of the complete low-fidelity matrix and the high-fidelity matrix, and the method of complementing and generating the
adversarial network through the multi-fidelity matrix is combined, so that the operation is simple and convenient, the practical
operability is high, and the application is wide.