The interTwin Digital Twin Engine (DTE) Thematic Modules for Physics enhance the capabilities of the DTE Core Modules for running the Digital Twins by adding functionalities to several fields, such as:
- Machine Learning (ML) based analysis for QCD simulation configurations and for time series
- Noise signals classification, noise analysis, de noising, and veto generation in Generative Adversarial Networks (GAN) based Lattice QCD configurations generation, noise simulation, particle detector simulation
- Particle physics validation techniques capable of assessing different aspects of model performance
- Fast simulation of High Energy Physics detectors