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We developed new parameterizations of local regularized finite-range pseudopotentials up to next-to-next-to-next-to-leading order (N3LO), used as generators of nuclear density functionals. When supplemented with zero-range spin-orbit and…
The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraints, which makes it impossible to solve explicitly. Stochastic…
Deep Gaussian Processes (DGPs) leverage a compositional structure to model non-stationary processes. DGPs typically rely on local inducing point approximations across intermediate GP layers. Recent advances in DGP inference have shown that…
This paper introduces a new model for highly accurate distribution voltage solutions, coined as a parameterized linear power flow model. The proffered model is grounded on a physical model of linear power flow equations, and uses…
Understanding many processes, e.g. fusion experiments, planetary interiors and dwarf stars, depends strongly on microscopic physics modeling of warm dense matter (WDM) and hot dense plasma. This complex state of matter consists of a…
This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic…
The Deep Underground Neutrino Experiment (DUNE) Phase-II Far Detector is considering an approximately 2000\,m$^2$ photon detection system to achieve a target mean light yield of 180\,PE/MeV. Meeting this requirement demands scalable,…
Hafnium hydride is a promising material for next-generation nuclear reactors, particularly as control rods for fast fission and shielding in fusion systems. The material's intrinsic brittleness encourages its use in the form of…
Plasma-based optics have emerged as a powerful platform for manipulating and amplifying ultra-intense laser pulses. However, the inherently nonlinear and dynamic nature of plasma leads to significant spatial, spectral, and temporal…
Density functional theory (DFT) is widely used to predict chemical properties, but its accuracy is limited by functional approximations and their approximate self-consistent densities. Density-corrected DFT (DC-DFT) is the study of the…
Based on the favourable properties of previously used one-dimensional (1D) atomic model potentials, we introduce a novel 1D atomic model potential for the 1D simulation of the quantum dynamics of a single active electron atom driven by a…
Relativistic energy density functionals (EDF) have become a standard tool for nuclear structure calculations, providing a complete and accurate, global description of nuclear ground states and collective excitations. Guided by the medium…
Head-Related Transfer Functions (HRTFs) have fundamental applications for realistic rendering in immersive audio scenarios. However, they are strongly subject-dependent as they vary considerably depending on the shape of the ears, head and…
This research explores the application of Deep Reinforcement Learning (DRL) to optimize the design of a nuclear fusion reactor. DRL can efficiently address the challenging issues attributed to multiple physics and engineering constraints…
The giant dipole resonance's (GDR) width and shape at finite temperature and angular momentum are described within the phonon damping model (PDM), which predicts an overall increase in the GDR's total width at low and moderate temperature…
Theoretical material investigation based on density functional theory (DFT) has been a breakthrough in the last century. Nevertheless, the optical properties calculated by DFT generally show poor agreement with experimental results…
Measurements of angular correlations in nuclear beta decay are important tests of the Standard Model (SM). Among those, the so-called D correlation parameter occupies a particular place because it is odd under time reversal, and because the…
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin orientations and…
Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data-driven methods, such as physics-informed neural networks…
We discuss the sensitivity of neutrino experiments at the luminosity frontier to generic hidden sectors containing new (sub)-GeV neutral states. The weak interaction of these states with the Standard Model can be efficiently probed through…