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physics.comp-ph2026
Gradient estimators for parameter inference in discrete stochastic kinetic models
Ludwig Burger, Annalena Kofler, Lukas Heinrich +1
Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For deterministic models, parameter inference often…
physics.comp-ph2024
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…