4 citations · 18 across the 21 of their papers we have counts for
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Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning
Tobias Kortus, Ralf Keidel, Nicolas R. Gauger +1
Reinforcement learning demonstrated immense success in modelling complex physics-driven systems, providing end-to-end trainable solutions by interacting with a simulated or real en…
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…
Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspective
Tobias Kortus, Ralf Keidel, Nicolas R. Gauger
Measurement and analysis of high energetic particles for scientific, medical or industrial applications is a complex procedure, requiring the design of sophisticated detector and d…
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…