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Temperature dependence of the neutron-nucleus interaction is known as theDoppler broadening of the cross-sections. This is a well-known effect due tothe thermal motion of the target nuclei that occurs in the neutron-nucleusinteraction. The fast computation of such effects is crucial for any nuclearapplication. Mechanisms have been developed that allow determining the Dopplereffects in the cross-section, most of them based on the numerical resolution ofthe equation known as Solbrig's kernel, which is a cross-section Dopplerbroadening formalism derived from a free gas atoms distribution hypothesis.This paper explores a novel non-linear approach based on deep learningtechniques. Deep neural networks are trained on synthetic and experimentaldata, serving as an alternative to the cross-section Doppler Broadening (DB).This paper explores the possibility of using physically informed neuralnetworks, where the network is physically regularized to be the solution of apartial derivative equation, inferred from Solbrig's kernel. The learningprocess is demonstrated by using the fission, capture, and scattering crosssections for $^{235}U$ in the energy range from thermal to 2250 eV.

Cross Section Doppler Broadening prediction using Physically Informed Deep Neural Networks