Accelerating the rate of discovery: Toward high-repetition-rate HED science
ORAL
Abstract
As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 Hz) come online around the world, the high-energy-density (HED) science they enable will experience a radical paradigm shift. The >1000x increase in shot rate over today’s shot-per-hour drivers translates into dramatically faster data acquisition and more experiments, and thus the potential to significantly accelerate the advancement of HED science.
Current energetic driver facilities depend on the ability to manually tune the lasers, the targets, the diagnostics settings, and more, between single shots or sets of shots through a manual feedback loop of data collection, data analysis, and optimization largely driven by experience and intuition. At 10 Hz, this paradigm is no longer sustainable as more complex data is collected more quickly than is possible to analyze manually.
Fully realizing the potential benefits of HRR facilities requires a fundamental shift in the design and execution of experiments done on them, the development of supporting technologies such as high-throughput targetry and diagnostics, and the evolution of machine learning techniques to couple traditional scientific computing with advanced data analytics. On-the-fly optimization of experiments will become ever more crucial as higher repetition rates will lead to more deliberate inter-shot variations and the improved operational range to allow exploration over larger regions of phase space.
We will present the vision and ongoing work to realize a HRR framework for rapidly delivered optimized experiments coupled to cognitive simulation to provide new insights in HED science.
Current energetic driver facilities depend on the ability to manually tune the lasers, the targets, the diagnostics settings, and more, between single shots or sets of shots through a manual feedback loop of data collection, data analysis, and optimization largely driven by experience and intuition. At 10 Hz, this paradigm is no longer sustainable as more complex data is collected more quickly than is possible to analyze manually.
Fully realizing the potential benefits of HRR facilities requires a fundamental shift in the design and execution of experiments done on them, the development of supporting technologies such as high-throughput targetry and diagnostics, and the evolution of machine learning techniques to couple traditional scientific computing with advanced data analytics. On-the-fly optimization of experiments will become ever more crucial as higher repetition rates will lead to more deliberate inter-shot variations and the improved operational range to allow exploration over larger regions of phase space.
We will present the vision and ongoing work to realize a HRR framework for rapidly delivered optimized experiments coupled to cognitive simulation to provide new insights in HED science.
*This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344, and supported by LDRDs 20-ERD-048 and 21-ERD-015, DOE Early Career SCW1651, and DOE-SC SCW1720 and SCW1722.
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Presenters
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Tammy Ma
- Lawrence Livermore Natl Lab
- Lawrence Livermore National Laboratory