Submission deadline extended: 

30 November 2022 

Original manuscripts are sought to the special issue on “Future Control Systems and Machine Learning at High Power Laser Facilities” of High Power Laser Science and Engineering (HPL),

The scope of this special issue is to highlight the cutting-edge engineering, computational and experimental developments supporting the next generation of high power laser facilities and enabling a paradigm shift in the design and analysis of high power laser experiments. The topics invited for inclusion are, but not limited to:

Data acquisition and facility control systems (e.g. Experiment synchronisation and data collection, meta-data management, robust data types and data standardisation across facilities etc.)

Big data storage, handling and access (e. g. Simulations and experimental data, interfacing with cloud computing, smart databases, quality standards for data repositories, accessing citizen science etc.)

Online analysis and feedback for experiments/simulations (e. g. Automated experiments, online optimisation of experimental output for applications and basic science, stabilisation of laser parameters and secondary sources quality over long-duration operation)

Machine learning (e.g Use of machine learning techniques for data analysis within laser-plasma experiments, topic specific tutorials on neural networks/gaussian regression, etc.)

 

Guest Editors:

Andreas Döpp, MPQ, Germany

Matthew Streeter, Queen’s University Belfast, U. K.

Scott Feister, California State University Channel Islands, USA

Hyung Taek Kim, Advanced Photonics Research Institute (APRI), Gwangju Institute of Science and Technology (GIST), South Korea

Co-ordinating Editor: Charlotte Palmer, Topical Editor, High Power Laser Science and Engineering

 

Submission deadline: 31 August 2022

Manuscripts should be submitted via the online submission system at: http://mc03.manuscriptcentral.com/clp-hpl. Please select “Special Issue on Future Control Systems and Machine Learning at High Power Laser Facilities” from the drop-down menu under “Manuscript Type” when submitting manuscript.