Modelling and computer simulation of particles and nuclides passage through matter and magnetic field, with Geant4 as example (3-7 September 2018, 3 ECTS)
Geant4 is a toolkit for simulating the passage of particles through matter. It
is the reference simulation engine in many areas. Geant4 covers all relevant
physics processes, electromagnetic, hadronic, decay, optical, for long and
short lived particles, for energy range spanning from tens of eV to TeV scale.
The transport of low energy neutrons down to thermal energies is also be
handled. The software can also simulate remnants of hadronic interactions,
including atomic de-excitation and provides extension to low energies down to
the DNA scale for biological modelling.
The course concerns the following topics: Introduction to simulation of elementary particles and nuclides passing through and interacting with matter; structure of a simulation program based on object-orientation; definition of realistic geometry including magnetic field; primary particles and interfaces to generators; electromagnetic and strong interaction physics processes; user interfaces; visualization; event biasing; simulation examples from subatomic physics, space science and medical applications.
Course responsible: Luis Sarmiento
Teachers: Makoto Asai (Stanford), to be confirmed: Alberto Ribon (CERN), Marc Verderi (IN2P3/LLR), Vladimir Ivanchenko (CERN)
Introduction to deep learning (4.5 ECTS)
Recent development in machine learning have led to a surge of interest in artificial neural networks (ANN). New efficient algorithms and increasingly powerful hardware has made it possible to create very complex and high-performing ANNs. The process of training such complex networks has become known as deep learning and the complex networks are typically called deep neural networks.
The aim of this course is to introduce students to common deep learnings architectues such as multi-layer perceptrons, convolutional neural networks and recurrent models such as the LSTM. Basic concepts in machine learning till also be introduced. The course consists of a series of lectures and computer exercises. The programming environment will be python (Jupyter notebook) together with the deep learning libraries Keras and Tensorflow.
Preliminary Schedule and content:
All lectures take place between 13:15 - 15:00 in seminar room HUB, theoretical physics.
The literature will consist of parts from the deep learning book and lecture notes.
Course responsible: Mattias Ohlsson
Teachers: Mattias Ohlsson + additional teachers and guest lecturers
Distributed computing concepts and tools (study period II, 4.5 ECTS)
The course gives introduction into concepts of geographically distributed computing, such as that implemented by research e-infrastructures dealing with large data volumes and high processing rates. The course is a combination of lectures and hands-on tutorials, addressing aspects of security, authentication and authorisation, interfaces to computing services, principles of distributed storage and data handling, resource characterisation and discovery, information representation and monitoring, workload management and scheduling in a distributed environment. It introduces concepts of services, interfaces, resource virtualisation, meta-protocols, non-interactive workloads, digital certificates and trust, Virtual Organisations, information systems and execution environments. Existing tools and services are introduced as well. The students will obtain personal certificates and a temporary access to a distributed computing infrastructure for exercises. Assessment is done on the basis of the course project, during which the students are expected to make use of distributed computing to solve their daily tasks.
Teachers: Oxana Smirnova, Balazs Konya, Florido Paganelli
Course responsible: Oxana Smirnova
Members of COMPUTE will be notified by e-mail when each course is open for
registration. For further questions please contact the study directors:
List of previous courses here.
This page was last modified on 10 September 2018.