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physics.comp-ph2020★ 18 cited
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations
Olivier Pannekoucke, Ronan Fablet
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network…
physics.comp-ph2019★ 18 cited
Convergence issues in derivatives of Monte Carlo null-collision integral formulations: a solution
J-M Tregan, S. Blanco, J. Dauchet +5
When a Monte Carlo algorithm is used to evaluate a physical observable A, it is possible to slightly modify the algorithm so that it evaluates simultaneously A and the derivatives…
physics.comp-ph2019★ 68 cited
Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres
Najda Villefranque, Fleur Couvreux, Richard Fournier +5
Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere fr…