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This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular…
The success of deep learning techniques over the last decades has opened up a new avenue of research for weather forecasting. Here, we take the novel approach of using a neural network to predict full probability density functions at each…
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a…
A deep neural network (DNN) has been developed to generate the distributions of nuclear charge density, utilizing the training data from the relativistic density functional theory and incorporating available experimental charge radii of…
In the impulse approximation regime the nuclear response to a weakly interacting probe can be written in terms of the measured nucleon structure fuctions and the target spectral function, yielding the energy and momentum distribution of the…
Free energies are fundamental quantities governing phase behavior and thermodynamic stability in polymer systems, yet their accurate computation often requires extensive simulations and post-processing techniques such as the Bennett…
A remarkably simple dependence of fragmentation cross-section on average binding energy has been established in experimental data. This dependence was empirically parametrised leading to a very useful formula for extrapolation. We find that…
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced,…
Understanding how nuclear size evolves with the number of protons and neutrons tests our models of strongly interacting matter. The nuclear charge (and proton) radii accessible through electromagnetic probes carry fundamental information on…
A new linked cluster expansion for the calculation of ground state observables of complex nuclei with realistic interactions has been developed [1-3]; using the V8' potential [4] the ground state energy, density and momentum distribution of…
A physics-informed neural network is presented for poroelastic problems with coupled flow and deformation processes. The governing equilibrium and mass balance equations are discussed and specific derivations for two-dimensional cases are…
Knowledge of $\nu_e$-$\mathrm{Fe}/\mathrm{Pb}$ differential cross sections for $\nu_e$ energy below several tens of MeV scale is believed to be crucial in understanding Supernova physics. In a segmented detector at Spallation Neutrino…
We introduce physics informed neural networks -- neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. In this two part…
Within the dinuclear system (DNS) conception, instead of solving Fokker-Planck Equation (FPE) analytically, the Master equation is solved numerically to calculate the fusion probability of super-heavy nuclei, so that the harmonic oscillator…
We develop the method for the calculation of the total reaction cross sections induced by the halo nuclei and stable nuclei. This approach is based on the Glauber theory, which is valid for nuclear reactions at high energy. It is extended…
Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning. However, as a data driven approach, the performance of deep kernel learning can still be restricted by scarce or insufficient data,…
Modern nuclear data evaluation increasingly requires not only accurate scattering calculations, but also efficient methods for uncertainty quantification and parameter optimization, tasks that benefit from differentiable solvers amenable to…
There is an increasing interest in quantifying the predictive power in nuclear structure calculations. We discuss how both experimental and systematic errors at the NN-level can be used to estimate the theoretical uncertainties by rather…
High precision cross-section data of the deuteron-proton breakup reaction at 130 MeV deuteron energy are compared with the theoretical predictions obtained with a coupled-channel extension of the CD Bonn potential with virtual Delta-isobar…
Deep neural networks (DNNs) are powerful tools for compressing and distilling information. Their scale and complexity, often involving billions of inter-dependent parameters, render direct microscopic analysis difficult. Under such…