J. L. Peterson
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View article: A Constrained Multi-Fidelity Bayesian Optimization Method
A Constrained Multi-Fidelity Bayesian Optimization Method Open
Recently, multi-fidelity Bayesian optimization (MFBO) has been successfully applied to many engineering design optimization problems, where the cost of high-fidelity simulations and experiments can be prohibitive. However, challenges remai…
View article: A Terminology for Scientific Workflow Systems
A Terminology for Scientific Workflow Systems Open
The term scientific workflow has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific appli…
View article: Toward digital design at the exascale: An overview of project ICECap
Toward digital design at the exascale: An overview of project ICECap Open
High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential…
View article: A multifidelity Bayesian optimization method for inertial confinement fusion design
A multifidelity Bayesian optimization method for inertial confinement fusion design Open
Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, t…
View article: A multifidelity Bayesian optimization method for inertial confinement fusion design
A multifidelity Bayesian optimization method for inertial confinement fusion design Open
Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical simulations to guide design. As simulation technology progresses, so too can the fidelity of models used to plan for new experiments. However, t…
View article: Impala: A Flexible Adaptive Sampling Algorithm for Active Learning
Impala: A Flexible Adaptive Sampling Algorithm for Active Learning Open
The Implicit Potential Active Learning Algorithm, or impala, is a novel approach to adaptive sampling that allows for the blending of multiple independent query strategies into a common batch active learning framework. This methodology is …
View article: 2022 Review of Data-Driven Plasma Science
2022 Review of Data-Driven Plasma Science Open
Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma scie…
View article: Investigating boosted decision trees as a guide for inertial confinement fusion design
Investigating boosted decision trees as a guide for inertial confinement fusion design Open
Inertial confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modeling and exploration of the experimental parameter space were essential to deepening our understan…
View article: Neural network surrogate models for equations of state
Neural network surrogate models for equations of state Open
Equation of state (EOS) data provide necessary information for accurate multiphysics modeling, which is necessary for fields such as inertial confinement fusion. Here, we suggest a neural network surrogate model of energy and entropy and u…
View article: Transfer learning driven design optimization for inertial confinement fusion
Transfer learning driven design optimization for inertial confinement fusion Open
Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and th…
View article: General Relativistic Implicit Monte Carlo Radiation-hydrodynamics
General Relativistic Implicit Monte Carlo Radiation-hydrodynamics Open
We report on a new capability added to our general relativistic radiation-magnetohydrodynamics code, Cosmos++ : an implicit Monte Carlo (IMC) treatment for radiation transport. The method is based on a Fleck-type implicit discretization of…
View article: 2022 Review of Data-Driven Plasma Science
2022 Review of Data-Driven Plasma Science Open
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and m…
View article: A Community Roadmap for Scientific Workflows Research and Development
A Community Roadmap for Scientific Workflows Research and Development Open
The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges …
View article: Three dimensional low-mode areal-density non-uniformities in indirect-drive implosions at the National Ignition Facility
Three dimensional low-mode areal-density non-uniformities in indirect-drive implosions at the National Ignition Facility Open
To achieve hotspot ignition, an inertial confinement fusion implosion must achieve high hotspot pressure that is inertially confined by a dense shell of DT fuel. This requires a symmetric implosion having high in-flight shell velocity and …
View article: An analytic asymmetric-piston model for the impact of mode-1 shell asymmetry on ICF implosions
An analytic asymmetric-piston model for the impact of mode-1 shell asymmetry on ICF implosions Open
For many years, low mode asymmetry in inertially confined fusion (ICF) implosions has been recognized as a potential performance limiting factor, but analysis has been limited to using simulations and searching for data correlations. Herei…
View article: Progress Towards Automating HYDRA Mesh Management via Reinforcement Learning
Progress Towards Automating HYDRA Mesh Management via Reinforcement Learning Open
of input data are to be analyzed are considered in this work. An O(N/sup 2/) time heuristic static allocation algorithm is presented to map the CFG to some CRG. The allocation algorithm provides a unique approach for reducing the interproc…
View article: Hotspot conditions achieved in inertial confinement fusion experiments on the National Ignition Facility
Hotspot conditions achieved in inertial confinement fusion experiments on the National Ignition Facility Open
We describe the overall performance of the major indirect-drive inertial confinement fusion campaigns executed at the National Ignition Facility. With respect to the proximity to ignition, we can describe the performance of current experim…
View article: Deep learning for NLTE spectral opacities
Deep learning for NLTE spectral opacities Open
Computer simulations of high energy density science experiments are computationally challenging, consisting of multiple physics calculations including radiation transport, hydrodynamics, atomic physics, nuclear reactions, laser–plasma inte…
View article: Special Issue on Machine Learning, Data Science, and Artificial Intelligence in Plasma Research
Special Issue on Machine Learning, Data Science, and Artificial Intelligence in Plasma Research Open
This Special Issue of the IEEE Transactions on Plasma Science (TPS) follows the first American Physical Society Division of Plasma Physics (APS-DPP) mini-conference on Machine Learning, Data Science, and Artificial Intelligence in Plasma R…
View article: Preparing Dense Net for Automated HYDRA Mesh Management via Reinforcement Learning
Preparing Dense Net for Automated HYDRA Mesh Management via Reinforcement Learning Open
Multi-physics HYDRA simulations for inertial confinement fusion (ICF) experiments at the National Ignition Facility use mesh relaxation directives to manage the state of the arbitrary Lagrangian-Eulerian (ALE) mesh and prevent entanglement…
View article: Transfer Learning to Model Inertial Confinement Fusion Experiments
Transfer Learning to Model Inertial Confinement Fusion Experiments Open
Inertial confinement fusion (ICF) experiments are designed using computer simulations that are approximations of reality and therefore must be calibrated to accurately predict experimental observations. In this article, we propose a novel …
View article: Merlin: Enabling Machine Learning-Ready HPC Ensembles
Merlin: Enabling Machine Learning-Ready HPC Ensembles Open
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows…
View article: Review of hydrodynamic instability experiments in inertially confined fusion implosions on National Ignition Facility
Review of hydrodynamic instability experiments in inertially confined fusion implosions on National Ignition Facility Open
Hydrodynamic instabilities are a major factor in degradation of inertial confinement fusion (ICF) implosions. In the highest performing implosions on National Ignition Facility, yield amplification (YA) due to alpha particle heating approa…
View article: Making inertial confinement fusion models more predictive
Making inertial confinement fusion models more predictive Open
Computer models of inertial confinement fusion (ICF) implosions play an essential role in experimental design and interpretation as well as our understanding of fundamental physics under the most extreme conditions that can be reached in t…
View article: Parameter inference with deep jointly informed neural networks
Parameter inference with deep jointly informed neural networks Open
A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment…
View article: Response to Comment on “Insulator-metal transition in dense fluid deuterium”
Response to Comment on “Insulator-metal transition in dense fluid deuterium” Open
In their comment, Desjarlais et al . claim that a small temperature drop occurs after isentropic compression of fluid deuterium through the first-order insulator-metal transition. We show that their calculations do not correspond to the ex…