Jim A. Gaffney, Kelli Humbird, Andrea Kritcher, Michael Kruse, Eugene Kur, Bogdan Kustowski, Ryan Nora, Brian Spears
{"title":"惯性约束聚变实验的缩放和点火数据驱动预测","authors":"Jim A. Gaffney, Kelli Humbird, Andrea Kritcher, Michael Kruse, Eugene Kur, Bogdan Kustowski, Ryan Nora, Brian Spears","doi":"10.1063/5.0215962","DOIUrl":null,"url":null,"abstract":"Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.","PeriodicalId":20175,"journal":{"name":"Physics of Plasmas","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven prediction of scaling and ignition of inertial confinement fusion experiments\",\"authors\":\"Jim A. Gaffney, Kelli Humbird, Andrea Kritcher, Michael Kruse, Eugene Kur, Bogdan Kustowski, Ryan Nora, Brian Spears\",\"doi\":\"10.1063/5.0215962\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.\",\"PeriodicalId\":20175,\"journal\":{\"name\":\"Physics of Plasmas\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physics of Plasmas\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0215962\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHYSICS, FLUIDS & PLASMAS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics of Plasmas","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1063/5.0215962","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, FLUIDS & PLASMAS","Score":null,"Total":0}
Data-driven prediction of scaling and ignition of inertial confinement fusion experiments
Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.
期刊介绍:
Physics of Plasmas (PoP), published by AIP Publishing in cooperation with the APS Division of Plasma Physics, is committed to the publication of original research in all areas of experimental and theoretical plasma physics. PoP publishes comprehensive and in-depth review manuscripts covering important areas of study and Special Topics highlighting new and cutting-edge developments in plasma physics. Every year a special issue publishes the invited and review papers from the most recent meeting of the APS Division of Plasma Physics. PoP covers a broad range of important research in this dynamic field, including:
-Basic plasma phenomena, waves, instabilities
-Nonlinear phenomena, turbulence, transport
-Magnetically confined plasmas, heating, confinement
-Inertially confined plasmas, high-energy density plasma science, warm dense matter
-Ionospheric, solar-system, and astrophysical plasmas
-Lasers, particle beams, accelerators, radiation generation
-Radiation emission, absorption, and transport
-Low-temperature plasmas, plasma applications, plasma sources, sheaths
-Dusty plasmas