Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions
Abstract In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and ini...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-10869-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849766635856986112 |
|---|---|
| author | Daniel A. Serino Evan Bell Marc Klasky Ben S. Southworth Balasubramanya Nadiga Trevor Wilcox Oleg Korobkin |
| author_facet | Daniel A. Serino Evan Bell Marc Klasky Ben S. Southworth Balasubramanya Nadiga Trevor Wilcox Oleg Korobkin |
| author_sort | Daniel A. Serino |
| collection | DOAJ |
| description | Abstract In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs. |
| format | Article |
| id | doaj-art-eab5777ca83d4212bf71f0f444dec534 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-eab5777ca83d4212bf71f0f444dec5342025-08-20T03:04:30ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-10869-3Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosionsDaniel A. Serino0Evan Bell1Marc Klasky2Ben S. Southworth3Balasubramanya Nadiga4Trevor Wilcox5Oleg Korobkin6Theoretical Division, Los Alamos National LaboratoryTheoretical Division, Los Alamos National LaboratoryTheoretical Division, Los Alamos National LaboratoryTheoretical Division, Los Alamos National LaboratoryComputer, Computational, and Statistical Sciences Division, Los Alamos National LaboratoryTheoretical Design Division, Los Alamos National LaboratoryTheoretical Division, Los Alamos National LaboratoryAbstract In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directly observable. What is observed instead is a time sequence of radiographic projections using X-rays. In this work, we define a set of sparse hydrodynamic features derived from the outgoing shock profile and outer material edge, which can be obtained from radiographic measurements, to directly infer such parameters. Our machine learning (ML)-based methodology involves a pipeline of two architectures, a radiograph-to-features network (R2FNet) and a features-to-parameters network (F2PNet), that are trained independently and later combined to approximate a posterior distribution for the parameters from radiographs. We show that the machine learning architectures are able to accurately infer initial conditions and EOS parameters, and that the estimated parameters can be used in a hydrodynamics code to obtain density fields, shocks, and material interfaces that satisfy thermodynamic and hydrodynamic consistency. Finally, we demonstrate that features resulting from an unknown EOS model can be successfully mapped onto parameters of a chosen analytical EOS model, implying that network predictions are learning physics, with a degree of invariance to the underlying choice of EOS model. To the best of our knowledge, our framework is the first demonstration of recovering both thermodynamic and hydrodynamic consistent density fields from noisy radiographs.https://doi.org/10.1038/s41598-025-10869-3 |
| spellingShingle | Daniel A. Serino Evan Bell Marc Klasky Ben S. Southworth Balasubramanya Nadiga Trevor Wilcox Oleg Korobkin Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions Scientific Reports |
| title | Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions |
| title_full | Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions |
| title_fullStr | Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions |
| title_full_unstemmed | Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions |
| title_short | Physics consistent machine learning framework for inverse modeling with applications to ICF capsule implosions |
| title_sort | physics consistent machine learning framework for inverse modeling with applications to icf capsule implosions |
| url | https://doi.org/10.1038/s41598-025-10869-3 |
| work_keys_str_mv | AT danielaserino physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions AT evanbell physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions AT marcklasky physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions AT benssouthworth physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions AT balasubramanyanadiga physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions AT trevorwilcox physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions AT olegkorobkin physicsconsistentmachinelearningframeworkforinversemodelingwithapplicationstoicfcapsuleimplosions |