4 citations · 6 across the 2 of their papers we have counts for
4 papers
-torch: differentiable scientific computing library
Muhammad F. Kasim, Sam M. Vinko
Physics-informed learning has shown to have a better generalization than learning without physical priors. However, training physics-informed deep neural networks requires some asp…
Time-resolved XUV Opacity Measurements of Warm-Dense Aluminium
S. M. Vinko, V. Vozda, J. Andreasson +20
The free-free opacity in plasmas is fundamental to our understanding of energy transport in stellar interiors and for inertial confinement fusion research. However, theoretical pre…
Efficient Parameter Sampling for Neural Network Construction
Drimik Roy Chowdhury, Muhammad Firmansyah Kasim
The customizable nature of deep learning models have allowed them to be successful predictors in various disciplines. These models are often trained with respect to thousands or mi…
Retrieving fields from proton radiography without source profiles
M. F. Kasim, A. F. A. Bott, P. Tzeferacos +3
Proton radiography is a technique in high energy density science to diagnose magnetic and/or electric fields in a plasma by firing a proton beam and detecting its modulated intensi…