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- W3208373997 abstract "Particle physics is a field equipped with a high-fidelity simulation that spans a hierarchy of scales ranging from the quantum mechanical interaction of fundamental particles to the electronic response of enormous particle detectors. These simulators provide a causal, generative model for the data. Moreover, they are are stochastic and non-differentiable. Most inference problems in particle physics can be framed as an inverse problem where the simulation represents the forward model. I will describe recent experiences and lessons learned from attacking these problems using deep learning and present examples of incorporating domain knowledge into the models." @default.
- W3208373997 created "2021-11-08" @default.
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- W3208373997 date "2019-03-13" @default.
- W3208373997 modified "2023-09-23" @default.
- W3208373997 title "Experience with Deep Learning in Particle Physics" @default.
- W3208373997 doi "https://doi.org/10.6084/m9.figshare.7840970.v1" @default.
- W3208373997 hasPublicationYear "2019" @default.
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