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An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers
The multi-messenger detection of the gravitational-wave signal GW170817, the corresponding kilonova AT2017gfo and the short gamma-ray burst...
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Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients
Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT)...
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Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations
This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with...
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Approximating the nuclear binding energy using analytic continued fractions
Understanding nuclear behaviour is fundamental in nuclear physics. This paper introduces a data-driven approach, Continued Fraction Regression (
cf-r ),...
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Vanquishing the computational cost of passive gamma emission tomography simulations leveraging physics-aware reduced order modeling
Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency to directly image the spatial distribution of...
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A model for organization and regulation of nuclear condensates by gene activity
Condensation by phase separation has recently emerged as a mechanism underlying many nuclear compartments essential for cellular functions. Nuclear...
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High-precision regressors for particle physics
Monte Carlo simulations of physics processes at particle colliders like the Large Hadron Collider at CERN take up a major fraction of the...
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Solving real-world optimization tasks using physics-informed neural computing
Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario...
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Physics and physicists at Banaras Hindu University: circa 1916–1960
Banaras also known as Varanasi and Kashi is one of the greatest centres of education and learning since ancient times. The city has been called as...
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Overhauser enhanced liquid state nuclear magnetic resonance spectroscopy in one and two dimensions
Nuclear magnetic resonance (NMR) is fundamental in the natural sciences, from chemical analysis and structural biology, to medicine and physics....
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Laser spectroscopy of triply charged 229Th isomer for a nuclear clock
Thorium-229 ( 229 Th) possesses an optical nuclear transition between the ground state ( 229g Th) and low-lying isomer ( 229m Th). A nuclear clock based on...
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Approaching a fully-polarized state of nuclear spins in a solid
Magnetic noise of atomic nuclear spins is a major source of decoherence in solid-state spin qubits. In theory, near-unity nuclear spin polarization...
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Gamma noise to non-invasively monitor nuclear research reactors
Autonomous nuclear reactor monitoring is a key aspect of the International Atomic Energy Agency’s strategy to ensure nonproliferation treaty...
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Physics-informed neural network reconciles Australian displacements and tectonic stresses
Stress orientation information is invaluable to evaluate active tectonic forces within the Earth’s crust. The global dataset provided by the World...
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Quantum nonlinear spectroscopy of single nuclear spins
Conventional nonlinear spectroscopy, which use classical probes, can only access a limited set of correlations in a quantum system. Here we...
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Expanding the limits of nuclear stability at finite temperature
Properties of nuclei in hot stellar environments such as supernovae or neutron star mergers are largely unexplored. Since it is poorly understood how...
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Observation of the radiative decay of the 229Th nuclear clock isomer
The radionuclide thorium-229 features an isomer with an exceptionally low excitation energy that enables direct laser manipulation of nuclear states....
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Magnetic field map** of inaccessible regions using physics-informed neural networks
A difficult problem concerns the determination of magnetic field components within an experimentally inaccessible region when direct field...
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Nuclear shell-model simulation in digital quantum computers
The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling...
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Stochastic gradient descent for optimization for nuclear systems
The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue...
