Physics
In this work, we compare three qubit-mapping strategies to study the structure of the nuclear ground state within the shell model description employing the Variational Quantum Eigensolver (VQE) approach. Although the initial point for…
This work presents calculations of thermal dilepton emission and polarization observables. It features a comprehensive framework which comprises virtual photon spectral functions complete at next-to-leading-order in the strong coupling and…
Network psychometrics conceptualises psychological constructs as emergent properties of systems of interacting variables. Energy-based probabilistic models have gained popularity as models of these interactions, but their psychometric…
We present a comprehensive benchmarking dataset and empirical scaling law analysis for neural network wavefunctions by matching them to a wide spectrum of famous many body target wavefunctions. The dataset, WF-Bench, spans multiple distinct…
This study presents a systematic investigation of the effects of isospin, including both T=0 and T=/0 components, on nucleon pairing correlations in fp-shell nuclei. To this aim, the interacting boson model-4, which explicitly incorporates…
A prior-informed large language model (LLM) driven multi-task learning framework is proposed for the unified description of multiple nuclear observables. By fine-tuning the pre-trained DeepSeek-R1-1.5B model with Low-Rank Adaptation (LoRA),…
We present a microscopic framework for predicting angular momentum distributions over the full range of fission fragment masses and charges. For the neutron-induced fission of $^{235}$U and $^{239}$Pu, the obtained distributions exhibit a…
Bayesian analyses in the context of relativistic heavy-ion collisions have so far relied almost exclusively on bulk hadronic observables constructed from momentum degrees of freedom to constrain the transport properties of the quark-gluon…
We present a method of studying few-body nuclear scattering by means of neural quantum states, without requiring time-evolution. A recently developed family of stable minimum principles for Schrodinger's equation provides conservative…
We present MARUT, a scalable multi-GPU computational fluid dynamics (CFD) framework designed for high-fidelity simulations of compressible flows spanning subsonic to hypersonic regimes, including chemically reacting nonequilibrium flows…
Binary neutron star mergers and proto-neutron stars provide unique environments where dense matter is hot, lepton rich, and potentially undergoes a transition from hadronic to deconfined quark matter. We investigate the thermodynamics and…
In the present work, we propose an improved harmonic oscillator model to systematically evaluate the proton radioactivity half-lives in spherical nuclei, incorporating centrifugal potential effects. By fitting the experimental data, the…
Charge radii are investigated along the Tin isotopic chain via ab initio Bogoliubov coupled cluster calculations at the singles and doubles level. In addition to the reproduction of absolute radii, the parabolic behavior of isotopic shifts…
This paper presents a deep learning strategy to simultaneously solve Partial Differential Equations (PDEs) and back-calculate their parameters in the context of deep tunnel excavation. A Physics-Informed Neural Network (PINN) model is…
The GW plus Bethe-Salpeter equation (GW-BSE) formalism is a well-established approach for calculating excitation energies and optical spectra of molecules, nanostructures, and crystalline materials. We implement GW-BSE in the CP2K code and…
In this work, we present a study on the effects of nuclear deformation ($\beta_2$,$\beta_3$) and surface diffuseness ($a$) on the charged hadron multiplicity ($N_{\mathrm{ch}}$) and elliptic flow ($v_2$), obtained in symmetric isobaric…
Finding high-quality trial wave functions for quantum Monte Carlo calculations of light nuclei requires a strong intuition for modeling the interparticle correlations as well as large computational resources for exploring the space of…
Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A…
We introduce an evidence-driven Bayesian formulation of physics-informed neural networks that enables automatic optimization of loss weights between PDE residuals, boundary conditions, and observational data. Unlike existing Bayesian PINN…
The azimuthal hadronic flow observed in ultra-relativistic ion-ion collisions provides a sensitive probe of many-body ground-state correlations in the colliding nuclei. In particular, collective correlations associated with nuclear…