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Giant dipole resonance (GDR) is one of the fundamental collective excitationmodes in nucleus. Continuous efforts have been made to the evaluation of GDRkey parameters in different nuclear data libraries. We introduced multitasklearning (MTL) approach to learn and reproduce the evaluated experimental dataof GDR key parameters, including both GDR energies and widths. Compared to thetheoretical GDR parameters in RIPL-3 library, the accuracies of MTL approachare almost doubled for 129 nuclei with experimental data. The significantimprovement is largely due to the right classification of unimodal nuclei andbimodal nuclei by the classification neural network. Based on the goodperformance of the neural network approach, an extrapolation to 79 nucleiaround the $\beta$-stability line without experimental data is made, whichprovides an important reference to future experiments and data evaluations. Thesuccessful application of MTL approach in this work further proofs thefeasibility of studying multi-output physical problems with multitask neuralnetwork in nuclear physics domain.

The description of giant dipole resonance key parameters with multitask neural networks