Surrogate generative models demonstrate extraordinary progress in current years. Although most applications are dedicated to image generation and similar commercial
goals, this approach is also very promising for natural sciences, especially for tasks like fast event simulation in HEP experiments. However, application of such generative models to scientific research implies specific requirements and expectations from these models. In the presentation, I'll discuss specific points which need attention when using generative models for scientific research. This includes ensuring that models satisfy different boundary conditions and match scientifically important but marginal statistics. We also need to establish procedures to evaluate the quality of the particular model, propagate model imperfection into systematic uncertainties of the final scientific result, and so on.
|Preferred contribution length||30 minutes|