A brief overview of a selection of HIFIS services that can be useful for Deep Learning applications, like the GPU Compute Service.
HIFIS provides and brokers digital services for everyone in Helmholtz and collaboration partners.
Large industrial facilities are complex systems that not only require regular maintenance and upgrades but are often inaccessible to humans due to various safety hazards. Therefore, a virtual reality (VR) system that can quickly replicate real-world remote environments to provide users with a high level of spatial and situational awareness is crucial for facility maintenance planning and robot...
I will present applications of Deep Learning-based protein structure prediction tools, such as AlphaFold, for interpreting experimental data in macromolecular crystallography and electron cryomicroscopy. I will also show examples of my own Deep Learning tools trained to complement the use of predicted models for macromolecular structure determination.
(1) Chojnowski NAR 2023 51(15)...
Particle colliders such as the LHC produce data at an unprecedented rate and volume. To overcome bandwidth constraints, event filtering systems are employed, with the first stage usually implemented in hardware using FPGAs. We present the first hardware demonstration of a real-time event filtering algorithm using machine learning for the Level-1 Trigger of the CMS experiment.
We present a case study of machine learning (ML) integrated into beamline control to drive autonomous x-ray reflectivity (XRR) measurements [1], which can be seen as a prototypical implementation to serve as an example for other in-situ and in-operando synchrotron and neutron experiments. ML strategies have significantly improved in the analysis of reflectometry data in recent years [2],...
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a significant computing constraint.
This contribution marks a significant breakthrough in this task by directly...
We present a model-agnostic search for new physics in the dijet final state using five different novel machine-learning techniques. Other than the requirement of a narrow dijet resonance, minimal additional assumptions are placed on the signal hypothesis. Signal regions are obtained utilizing multivariate machine learning methods to select jets with anomalous substructure. A collection of...
