Plenary Event
Tuesday Plenary Session
25 April 2023 • 08:50 - 10:30 CEST | Nadir
8:50 to 8:55
Welcome and Introduction
Bedrich Rus, ELI Beamlines (Czech Republic)
8:55 to 9:40
Fusion ignition at the National Ignition Facility
Thomas Spinka
Lawrence Livermore National Lab. (United States)
On December 5th, 2022, the National Ignition Facility in Livermore, California, USA performed the first experiment demonstrating controlled fusion ignition in the laboratory. With a 2.05MJ UV laser drive energy delivered to the target, a neutron yield of 3.15MJ was released by the fusion reactions in the capsule, providing a net target gain of ~1.5×. The results of this experiment will be discussed, along with the decades-long developments in optical materials, laser architectures, target fabrication, and target diagnostics enabling this recent accomplishment. We will discuss the next steps for NIF and provide an outlook on future applications and technologies, including the reinvigorated pursuit of Inertial Fusion Energy.
Thomas Spinka is the Program Element Leader for Laser Development in Lawrence Livermore National Laboratory’s Advanced Photon Technologies group. He leads the group’s laser R&D activities, and identifies and fosters new laser-driven capabilities for US National Nuclear Security Administration and Department of Energy missions. These activities include the development, design, construction, and deployment of high pulse energy, high peak power, and high average power diode-pumped solid-state lasers and the development of new high efficiency and high average power laser architectures and technologies. Spinka received a B.S. in Electrical and Computer Engineering and a B.S. in Astronomy along with M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.
9:45 to 10:30
AI and deep learning for microscopy
Giovanni Volpe
University of Gothenburg (Sweden)
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions.
To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
Giovanni Volpe is Full Professor at the Physics Department of the University of Gothenburg University, where he leads the Soft Matter Lab (http://softmatterlab.org). His research interests include soft matter, optical trapping and manipulation, statistical mechanics, brain connectivity, and machine learning. He has authored more than 100 articles and reviews on soft matter, statistical physics, optics, physics of complex systems, brain network analysis, and machine learning. He co-authored the books "Optical Tweezers: Principles and Applications" (Cambridge University Press, 2015) and “Simulation of Complex Systems” (IOP Press, 2021). He has developed several software packages for optical tweezers (OTS — Optical Tweezers Software), brain connectivity (BRAPH—Brain Analysis Using Graph Theory), and microscopy enhanced by deep learning (DeepTrack).
Welcome and Introduction
Bedrich Rus, ELI Beamlines (Czech Republic)
8:55 to 9:40
Fusion ignition at the National Ignition Facility
Thomas Spinka
Lawrence Livermore National Lab. (United States)
On December 5th, 2022, the National Ignition Facility in Livermore, California, USA performed the first experiment demonstrating controlled fusion ignition in the laboratory. With a 2.05MJ UV laser drive energy delivered to the target, a neutron yield of 3.15MJ was released by the fusion reactions in the capsule, providing a net target gain of ~1.5×. The results of this experiment will be discussed, along with the decades-long developments in optical materials, laser architectures, target fabrication, and target diagnostics enabling this recent accomplishment. We will discuss the next steps for NIF and provide an outlook on future applications and technologies, including the reinvigorated pursuit of Inertial Fusion Energy.
Thomas Spinka is the Program Element Leader for Laser Development in Lawrence Livermore National Laboratory’s Advanced Photon Technologies group. He leads the group’s laser R&D activities, and identifies and fosters new laser-driven capabilities for US National Nuclear Security Administration and Department of Energy missions. These activities include the development, design, construction, and deployment of high pulse energy, high peak power, and high average power diode-pumped solid-state lasers and the development of new high efficiency and high average power laser architectures and technologies. Spinka received a B.S. in Electrical and Computer Engineering and a B.S. in Astronomy along with M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.
9:45 to 10:30
AI and deep learning for microscopy
Giovanni Volpe
University of Gothenburg (Sweden)
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions.
To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
Giovanni Volpe is Full Professor at the Physics Department of the University of Gothenburg University, where he leads the Soft Matter Lab (http://softmatterlab.org). His research interests include soft matter, optical trapping and manipulation, statistical mechanics, brain connectivity, and machine learning. He has authored more than 100 articles and reviews on soft matter, statistical physics, optics, physics of complex systems, brain network analysis, and machine learning. He co-authored the books "Optical Tweezers: Principles and Applications" (Cambridge University Press, 2015) and “Simulation of Complex Systems” (IOP Press, 2021). He has developed several software packages for optical tweezers (OTS — Optical Tweezers Software), brain connectivity (BRAPH—Brain Analysis Using Graph Theory), and microscopy enhanced by deep learning (DeepTrack).