8 citations · 31 across the 19 of their papers we have counts for
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Efficient Forward-Mode Algorithmic Derivatives of Geant4
Max Aehle, Xuan Tung Nguyen, Mihály Novák +5
We have applied an operator-overloading forward-mode algorithmic differentiation tool to the Monte-Carlo particle simulation toolkit Geant4. Our differentiated version of Geant4 al…
Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower Simulations
Max Aehle, Mihály Novák, Vassil Vassilev +4
Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Co…
Hybrid parallel discrete adjoints in SU2
Johannes Blühdorn, Pedro Gomes, Max Aehle +1
The open-source multiphysics suite SU2 features discrete adjoints by means of operator overloading automatic differentiation (AD). While both primal and discrete adjoint solvers su…