A physics informed emulator for laser-driven radiating shock simulations

McClarren, Ryan G.; Ryu, Dongseok; Drake, R. P.; Grosskopf, M.J.; Bingham, Derek; Chou, Chuan-Chih; Fryxell, B.; Holst, B. van der; Holloway, James Paul; Kuranz, Carolyn; Mallick, Bani K.; Rutter, Erica M.; Torralva, Ben

doi:10.1016/j.ress.2010.08.012

Cited by 15 publications

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“…Details regarding the radiative shock physics can be found in Drake et al (2011). The radiating shock experiments that we are concerned with can be viewed as small-scale experiments for understanding astrophysical shock waves and other high temperature phenomena (McClarren et al, 2011;Drake et al, 2011). Several measurements of interest are taken from each shock experiment and also simulations.…”

Section: Crash Applicationmentioning

confidence: 99%

“…These shocks are considered radiative when the radiation energy flux from the shock is high enough to impact the structure of the shock wave. Details regarding the radiative shock physics can be found in Drake et al (2011). The radiating shock experiments that we are concerned with can be viewed as small-scale experiments for understanding astrophysical shock waves and other high temperature phenomena (McClarren et al, 2011;Drake et al, 2011).…”

Section: Crash Applicationmentioning

confidence: 99%

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Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators

et al. 2013

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Computer codes are widely used to describe physical processes in lieu of physical observations. In some cases, more than one computer simulator, each with different degrees of fidelity, can be used to explore the physical system. In this work, we combine field observations and model runs from deterministic multi-fidelity computer simulators to build a predictive model for the real process. The resulting model can be used to perform sensitivity analysis for the system, solve inverse problems and make predictions. Our approach is Bayesian and will be illustrated through a simple example, as well as a real application in predictive science at the Center for Radiative Shock Hydrodynamics at the University of Michigan.KEY WORDS: Computer Experiment; Gaussian process; Markov Chain Monte Carlo. arXiv:1208.2716v1 [stat.AP] 13 Aug 2012 occur, for example, because of the presence of reduced order physics in lower fidelity models, different levels of accuracy specified for numerical solvers or solutions obtained on finer grids. In these cases, a higher fidelity model is thought to better represent the physical process than a lower fidelity model, but also takes more computer time to produce an output than a lower fidelity model. So, combining relatively cheap lower fidelity model runs with more costly high fidelity runs to emulate the high fidelity model has been an significant problem of interest (Kennedy and O'Hagan, 2000;Qian et al., 2006 and.Another important application of computer models is that of calibration (e.g., Kennedy and O'Hagan, 2001;Higdon et al., 2004) where the aim is to combine simulator outputs with physical observations to build a predictive model and also estimate unknown parameters that govern the behaviour of the computer model. The latter endeavour amounts to solving a sort of inverse problem, while the former activity is a type of regression problem.Motivated by applications at the Center for Radiative Shock Hydrodynamics (CRASH) at the University of Michigan, the aim of this work is to develop new methodology to combine outputs from simulators with different levels of fidelity and field observations to make predictions of the physical system with associated measurements of uncertainty. In the spirit similar to Kennedy and O'Hagan (2000 and2001) and Higdon et al. (2004), we propose a predictive model that incorporates computer model outputs and field data, while attempting to find optimal values for some input parameters (i.e. calibration parameters). Different models are specified for each source of data (Kennedy and O'Hagan, 2000;Qian et al., 2006 and. The approach calibrates each computer model to the next highest level of fidelity model, and the simulator of the highest fidelity is then calibrated to the field measurements. All the response surfaces are Gaussian process (GP) models and the various sources of information that inform predictions of the physical system are combined with a Bayesian hierarchical model. The paper is organized as follows: In section 2, we will introduce the prop...

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Section: Crash Applicationmentioning

confidence: 99%

Section: Crash Applicationmentioning

confidence: 99%

Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators

et al. 2013

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“…A key underlying assumption to all laser-driven highenergy-density (HED) experiments is that the important dynamics of the system are repeatable over the course of multiple experiments as long as the defining metrics of the platform remain constant. [1][2][3][4] Experiment repeatability allows one to map the entire evolution of a system, even when diagnostics are only capable of capturing portions of a single experiment due to limitations like small-temporal or spatial windows compared to the experiment scales. If the platform is repeatable, then measurements of the repeatable dynamics from multiple experiments can be treated as measurements of the same system.…”

Section: Introductionmentioning

confidence: 99%

Demonstration of repeatability in a high-energy-density planar shear mixing layer experiment

Merritt

Doss

Stéfano

et al. 2017

High Energy Density Physics

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“…The approach has been designed to couple with existing radiation-hydrodynamics simulation codes without modification; in fact simulations are treated as a 'black box' making the method applicable to a large class of difficult data analysis problems. This 'brute force' approach avoids the use of the complex fitting functions used in other methods [6,7], relying on the simulations to describe the very rapidly changing properties of ICF capsules close to ignition. This has allowed us to develop a simple implementation of our analysis approach and echos the previous work in the field using large simulation databases.…”

Section: Introductionmentioning

confidence: 99%

Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF

et al. 2013

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The complex nature of inertial confinement fusion (ICF) experiments results in a very large number of experimental parameters that are only known with limited reliability. These parameters, combined with the myriad physical models that govern target evolution, make the reliable extraction of physics from experimental campaigns very difficult. We develop an inference method that allows all important experimental parameters, and previous knowledge, to be taken into account when investigating underlying microphysics models. The result is framed as a modified χ 2 analysis which is easy to implement in existing analyses, and quite portable. We present a first application to a recent convergent ablator experiment performed at the NIF, and investigate the effect of variations in all physical dimensions of the target (very difficult to do using other methods). We show that for well characterised targets in which dimensions vary at the 0.5% level there is little effect, but 3% variations change the results of inferences dramatically. Our Bayesian method allows particular inference results to be associated with prior errors in microphysics models; in our example, tuning the carbon opacity to match experimental data (i.e., ignoring prior knowledge) is equivalent to an assumed prior error of 400% in the tabop opacity tables. This large error is unreasonable, underlining the importance of including prior knowledge in the analysis of these experiments. (J.A. Gaffney) cluded, along with prior work on microphysics models, in 20 a consistent and efficient analysis. The approach has been 21 designed to couple with existing radiation-hydrodynamics 22 42 of important variables and prior knowledge has a dra-43 matic effect on the results of inference (even if they are 44 purely sources of noise). Our method allows these effects 45 to be included with almost no computational overhead. 46 We demonstrate this by presenting an analysis of a single 47 NIC convergent ablator (conA) shot [8, 9], N110625. We 48 find that variations in the dimensions of the target can 49 have a dramatic effect on the inferred drive and carbon 50 opacity, although this is mitigated by thorough metrology 51 of the target. Our method also allows prior knowledge to 52 be included and in the case of ICF we find that this is an 53 extremely important factor. We use it to investigate the 54 implied error in microphysics models associated with ne-55 glecting this prior work. The inclusion of this prior knowl-56 edge is an important strength of the Bayesian method, as 57 it provides context for observed data and therefore allows 58 meaningful information to be inferred even from a sin-59 gle experimental result. The total information from a set 60 of experiments can be viewed as a series of such single-61 shot inferences, allowing the analysis performed here to 62 be generalised to full experimental campaigns very easily. 63 The approach is to treat the output of the simula-64 tion code as probabilistic, and to apply standard meth-65 ods of Bayesian analysis ...

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A physics informed emulator for laser-driven radiating shock simulations

Cited by 15 publications

References 18 publications

Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators

Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators

Demonstration of repeatability in a high-energy-density planar shear mixing layer experiment

Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF

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