We introduce an extension of the hopping method, typically used in quantum systems, to mechanical networks for constructing dynamical matrices. This innovative and efficient approach facilitates the ...
We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to ...
When it comes to quant finance, few tasks evoke as much existential dread in quants as calculating sensitivities for the purposes of FRTB or XVA. These sensitivities that tell risk managers how jumpy ...
Modeling strong shock waves in fluids remains a persistent challenge in computational physics. Essential to research efforts in industry and defense, numerous methods have been devised to improve the ...
Abstract: This paper presents PyWaveProp, a library for wave propagation modeling, and highlights its application features in tropospheric radio wave propagation. The one-way Helmholtz equation method ...
Micromagnetic simulations are widely used in a range of applications, from magnetic storage technologies and the design of hard and soft magnetic materials, to the modern fields of magnonics, ...
Physical scientists and engineering research and development (R&D) teams are embracing neural networks in attempts to accelerate their simulations. From quantum mechanics to the prediction of blood ...
In undergraduate physical chemistry, Schrödinger’s equation is solved for a variety of cases. In doing so, the energies and wave functions of the system can be interpreted to provide connections with ...
Electron energy-loss spectroscopy (EELS) is a unique tool that is extensively used to investigate the plasmonic response of metallic nanostructures. We present here a novel approach for EELS ...
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