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Modern laboratory techniques like ultrafast laser excitation and shock compression can bring matter into highly nonequilibrium states with complex structural transformation, metallization and dissociation dynamics. To understand and model…
A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…
We introduce a novel method for studying systematic trends in nuclear reaction data using generative adversarial networks. Libraries of nuclear cross section evaluations exhibit intricate systematic trends across the nuclear landscape, and…
By considering three different Nucleon-Nucleon (NN) elastic differential cross sections: the Cugnon \emph{et al.} parameterized differential cross section [Nucl. Instrum. Methods Phys. Res., Sect. \textbf{B111}, 215 (1996)], and the…
Theoretical study of systematics of neutron scattering cross sections on various materials for neutron energies up to several hundred MeV are of practical importance. In this paper, we analysed various cross sections of neutron-nucleus…
Couplings between relative motion and internal structures are known to affect fusion barriers by dynamically modifying the densities of the colliding nuclei. The effect is expected to be stronger at energies near the barrier top, where…
Nucleon-nucleon (NN) cross sections are evaluated in neutron-rich matter using a scaling model according to nucleon effective masses. It is found that the in-medium NN cross sections are not only reduced but also have a different isospin…
Disentangling coherent and incoherent effects in the photoemission spectra of strongly correlated materials is generally a challenging problem due to the involvement of numerous parameters. In this study, we employ machine learning…
The calculation of the energy-, density-, and isospin-dependent $\Delta$ production cross sections in nucleon-nucleon (NN) scattering $\sigma^*_{NN\rightarrow N\Delta}$ has been performed within the framework of the relativistic BUU…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method…
In this work, coherence effects in deep inelastic scattering (DIS) and in the Drell-Yan (DY) process off nuclei are investigated, in particular nuclear shadowing. The target rest frame and the color dipole formulation are employed. Multiple…
Antiproton-nucleon ($\bar{p}N$) total cross sections are typically 3-4 times larger than the $NN$ ones at incident energies from a few hundreds to thousands MeV. We investigate antiproton-nucleus scattering as it could work as a probe of…
The nucleon-nucleon ($NN$) inelastic cross section plays an important role in constraining the nuclear equation of state at high baryon density and in describing the formation and evolution of compact astrophysical objects. In this study,…
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with…
In the target rest frame and at high energies, Drell-Yan (DY) dilepton production looks like bremsstrahlung of massive photons, rather than parton annihilation. The projectile quark is decomposed into a series of Fock states. Configurations…
A modified physics-informed neural network is used to predict the dynamics of optical pulses including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear Schr\"odinger equation in birefringent fibers. At the same time,…
Dosimetry cross sections are fundamental quantities essential in determination of neutron fluences in points of interest in technologies under heavy radiation load. The most common is their application to Reactor Pressure Vessel aging…
Existing data on near-barrier fusion excitation functions of medium and heavy nucleus-nucleus systems have been analyzed using a simple diffused barrier formula derived assuming the Gaussian shape of the barrier height distributions. Fusion…
Time-dependent partial differential equations are a significant class of equations that describe the evolution of various physical phenomena over time. One of the open problems in scientific computing is predicting the behaviour of the…