Pith. sign in

REVIEW 4 major objections 7 minor 1 cited by

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A slow-plus-fast pair of impact shots, not a single fast one, carries enough information to recover all nine material parameters from radiographs.

desk verdict Useful experimental-design claim (low+high velocity) but the load-bearing negative result is backed by a single VAE fit; still deserves serious review. read the letter →

arxiv 2506.23914 v1 pith:X4TBAK6D submitted 2025-06-30 physics.comp-ph cs.LG

Evan Bell , Daniel A. Serino , Ben S. Southworth , Trevor Wilcox , Marc L. Klasky This is my paper
classification physics.comp-phcs.LG
keywords flyerplateimpactporousmaterialsMie-GrüneisenequationofstateP-alphacrushmodelvariationalautoencoderradiographicparameterestimationdensityreconstructionshockphysics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks what set of flyer-plate impact observations is enough to recover the material parameters of a porous metal from radiographs. It argues that fast impacts alone cannot do the job: even with perfectly resolved density fields, or a time sequence of them, the crush-model parameters are effectively invisible because the shocked material is either fully compacted or untouched. A slow impact that partially crushes the pores plus a fast impact that drives a strong shock is sufficient, and the paper demonstrates this by training a density-to-parameters variational autoencoder on simulated experiments and showing that the slow-plus-fast combination is the only tested data space in which all nine parameters are accurately inferred. It then introduces a radiograph-to-parameters version of the same architecture that outputs a posterior over parameters directly from noisy radiographs, and shows that feeding posterior samples through a hydrodynamic solver gives physically admissible density reconstructions. If the central claim is right, it tells shock-physics experimenters which two shots to fire and provides a practical analysis route that skips density reconstruction altogether.

What carries the argument

The load-bearing object is a conditional variational autoencoder, used in two variants: D2P-VAE (density fields to parameters) and R2P-VAE (radiographs to parameters). Its encoder compresses the image, a parameter encoder maps true parameters to a latent Gaussian, and a decoder reconstructs the parameters; training uses the σ-VAE objective, which estimates the reconstruction variance per batch rather than requiring a hand-tuned β. At inference, the decoder samples from an approximate posterior over the nine unknown material parameters. The argumentative mechanism is the use of the same architecture as an information probe: for each candidate data space (time series, velocity choices, clean or noisy radiographs), near-zero correlation between predicted and true values is read as absence of a well-posed map, because the training data are large and the architecture is state-of-the-art. Physically, the slow-plus-fast pair spans the two regimes that matter—partial pore compaction on the elastic branch of the P–α response and full compaction with a strong shock—so the observable exercises both the partial-compaction curve and the fully shocked Hugoniot (the shock-compression relation).

What would settle it

Repeat the high-velocity-only density-to-parameter experiment across ten random seeds and doubled network capacity; if any seed or capacity configuration recovers the crush parameters $P_s$, $P_e$, and $n$ with held-out correlation above 0.9, the claim that this data space carries no information for them is refuted.

Watch

Extended reading notes

Core claim

The central discovery is an information-structure statement: a well-posed map from a single high-velocity impact observation to the full set of Mie-Grüneisen equation-of-state and P–α crush parameters does not exist, regardless of whether the observable is a final density field or a dynamic sequence of density fields. The paper supports this by training the density-to-parameters VAE on thousands of simulations and reporting near-zero correlation for the crush parameters $P_s$, $P_e$, and $n$ at the high impact velocity of $5\cdot10^5$ cm/s, while the same parameters are recovered with correlations above 0.99 when a low-velocity experiment at $5\cdot10^4$ cm/s is added. The physical reason given is that the high-velocity final state consists only of fully compacted or nearly uncompressed aluminum, so the multi-parameter compaction curve is not exercised. Moving to noisy radiographs, the second shock is obscured by radiographic noise, which explains why the shock parameters $c_s$ and $s$ and the exponent $n$ become harder to infer; nevertheless the posteriors are well calibrated overall, and density fields reconstructed by running sampled parameters through the hydrocode are accurate, with errors concentrated at material interfaces. The paper also claims graceful degradation under out-of-distribution noise and under an entirely different equation of state (Sesame tables), where the inferred parameters yield reconstructions closer to the true density fields than any training density field.

Load-bearing premise

The conclusion that high-velocity data contain no crush information rests on trusting that the trained density-to-parameters network is expressive enough to learn any well-posed map that actually exists; if the network underfits the crush parameters, the near-zero correlations would be model failure rather than evidence of missing information.

Editorial extensions

If this is right

  • A two-shot campaign—one low-velocity and one high-velocity flyer-plate impact—is sufficient, in the simulated setting, to infer all nine Mie-Grüneisen and P–α parameters from a single pair of radiographs.
  • Noisy radiographs degrade inference most for the sound speed $c_s$, the slope $s$, and the crush exponent $n$, because radiographic noise hides the second shock; shot designs that keep that feature visible would recover these parameters.
  • Running posterior parameter samples through a hydrodynamic solver produces density fields that obey conservation laws and match a dedicated image-to-density network in accuracy, while also supplying a per-pixel uncertainty estimate.
  • The VAE's predictive posterior is calibrated well enough overall to be used as an experimental uncertainty proxy, with only mild overconfidence for $\rho_p$ and underconfidence for $P_e$.
  • The pipeline degrades gracefully rather than failing when radiographs carry out-of-distribution noise or when the true equation of state is not the one used in training.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Going beyond the paper, the two-regime requirement is probably a property of the physics, not of this dataset: any experiment that observes only fully compacted or undisturbed material will leave crush parameters unidentifiable no matter how many images are taken, which a formal identifiability analysis of the forward map could prove directly.
  • The authors' correlation-probe methodology is itself a reusable test: train the ideal-observable network, inspect held-out correlation, and if no architecture or seed change recovers a parameter, the observation lacks information; a more rigorous variant would compute the rank of the parameter-to-observable Jacobian.
  • The experimental design could be made adaptive: instead of pre-committing to one slow and one fast shot, use the posterior entropy of the VAE to choose the next impact velocity, which would handle materials where the two fixed velocities do not span both regimes.
  • Before field deployment, the synthetic radiographic noise model needs validation against real detector behavior; the out-of-distribution test is a useful first stress test but not a substitute for measured scatter and spectral effects.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. This paper presents a machine-learning framework for inferring nine Mie-Grüneisen equation-of-state and P-alpha crush model parameters in simulated flyer plate impact experiments from density fields or synthetic radiographs. The authors train conditional variational autoencoders (D2P-VAE and R2P-VAE) on 30,000 CTH simulations at three impact velocities, and claim that a single high-velocity observation, even with full density fields or a time sequence, does not provide enough information to identify the crush parameters Ps, Pe, and n, whereas combining one low- and one high-velocity experiment enables robust inference of all parameters. They then use the inferred parameters to drive forward CTH simulations, yielding physically admissible density reconstructions that are competitive with a direct image-to-density U-Net, and demonstrate robustness to out-of-distribution noise and a mismatched (Sesame) equation of state.

Significance. If the central negative claim holds, the paper would provide a concrete, actionable experimental design for calibrating porosity and EoS models in dynamic compression experiments, and the proposed R2P-VAE pipeline offers a practical route to parameter inference directly from radiographs. The paper has several strengths: the forward model is realistic and carefully specified; the evaluation uses held-out test sets; the R2P-VAE posterior is checked with empirical coverage calibration (Figure 9); and the density-reconstruction comparison against a U-Net baseline, including model-mismatch tests, is informative. However, the load-bearing negative claim about high-velocity insufficiency is inferred from a single VAE fit, and the paper does not provide a model-independent identifiability or sensitivity analysis for the three crush parameters in the high-velocity regime. Consequently, the significance of the experimental-design recommendation is currently conditional on establishing that the observed failure is an information limitation rather than an estimator limitation.

major comments (4)
  1. [Section 3.1 (Table 4, Figure 4)] The central negative claim that a single high-velocity observation is informationally insufficient for Ps, Pe, and n rests entirely on the performance of one D2P-VAE architecture with one random initialization. The text states 'Assuming that the D2P-VAE samples from the true posterior of parameter values for a given density field, these point estimates will be optimal in terms of MSE and r2,' but no posterior calibration check, repeated-seed training, or capacity-bounding experiment is provided for the D2P-VAE. I request the following additions: (a) re-training with multiple seeds and reporting the spread of r and MAPE values in Table 4; (b) a capacity-ablation study (e.g., larger latent dimension or more convolutional channels) to show that the near-zero correlations for Ps, Pe, and n persist; (c) a control experiment using uninformative inputs (e.g., parameter-independent or randomized density fields) to demonstrate that the network can produce high correlations when information is present and low correlations when it is not; and (d) D2P-VAE empirical coverage plots analogous to Figure 9. Without these, the observed near-zero correlations could equally reflect underfitting, posterior misspecification, or optimization failure, and the claim of 'high confidence' in the abstract and in Section 3.1 is not supported.
  2. [Section 3.1 (Figures 4 and 7)] The physical explanation for the presumed insufficiency is that the high-velocity density field contains only fully compacted and nearly uncompressed material, which does not 'span the potential compaction dynamics,' but this is asserted rather than demonstrated. For cs and s, the authors provide sensitivity line-outs in Figure 7 that help explain partial recoverability; no analogous sensitivity analysis is shown for Ps, Pe, and n in the high-velocity regime. Please add a study that varies Ps, Pe, and n individually over their Table 3 ranges (and, if helpful, over exaggerated ranges as in Figure 7) and plots the resulting density-field and radiograph line-outs. If those parameters have no measurable effect on the high-velocity observables, the information-theoretic claim would be substantially strengthened. If they do have an effect, the failure of the D2P-VAE to recover them would indicate an estimator deficiency, changing the paper's main conclusion.
  3. [Section 3.1 (paragraph beginning 'We observe that...')] The statement 'Due to the large quantity of training data and use of state-of-the-art ML architectures, these results provide high confidence that a well-posed mapping ... cannot be constructed' overstates what can be inferred from a single model fit. The D2P-VAE is a Gaussian latent-variable model with a specific capacity and regularization; neither the quantity of training data nor the use of a well-known architecture rules out underfitting or undesirable local optima. I recommend either tempering this claim to state that the results provide evidence for the difficulty within the chosen model class, or adding direct support such as training and test loss curves, a training-set-size study, and a comparison against a simpler linear regression baseline. Alternatively, an independent, non-ML identifiability analysis (e.g., a local Fisher information or Cramér-Rao bound for the high-velocity observable) would place the negative claim on firmer ground.
  4. [Section 3.1 (high-velocity time-series experiment)] The conclusion that a 'dynamic sequence of images' from a single high-velocity experiment does not help resolve Ps, Pe, or n is based on exactly one temporal sampling schedule, t in {0, 2, 4, 6, 8, 10, 12} microseconds. Because the compaction process may occur on a timescale not resolved by this schedule, the claim is too broad. Please either test additional schedules (e.g., more frames at earlier times during the compaction phase) or explicitly qualify the conclusion to the schedules considered. This is particularly relevant because the stated purpose of the experiment is to determine 'sufficient conditions' for parameter inference.
minor comments (7)
  1. [Section 1.1] The word 'high-fidelty' should be 'high-fidelity'.
  2. [Table 1 caption] The phrase 'Initial geometry the of flyer plate experiment' should be 'Initial geometry of the flyer plate experiment'.
  3. [Section 2.1] The phrase 'and n is an parameter' should be 'and n is a parameter'.
  4. [Section 3.1] The phrase 'we consider a training the D2P-VAE' should be 'we consider training the D2P-VAE'.
  5. [Section 2.1] The material strength model is referred to as 'V on Mises'; the correct spelling is 'von Mises'.
  6. [Table 4 caption] If the table is printed in grayscale, the red highlighting of low-performing entries is lost; please also mark those entries with an asterisk or boldface.
  7. [Figure 7] The varied cs and s ranges extend far beyond the prior ranges in Table 3; the authors acknowledge this, but it would be useful to also show variations within the actual ranges to assess distinguishability under realistic priors.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the identifiability claim is an empirical, held-out finding, not a construction-level tautology.

full rationale

The paper's central claim—that a single high-velocity density field (or a time sequence of such fields) does not suffice to infer all nine Mie-Grüneisen and P–α parameters—is obtained by training a D2P-VAE on 8,000 forward CTH simulations with parameters drawn uniformly from stated priors, then evaluating posterior-mean point estimates on a disjoint 1,000-case test set (Figure 4, Table 4). Nothing is fitted to the test labels or to the conclusion; the near-zero correlations for Ps, Pe, and n are measured against held-out ground truth. The proposed combined high-plus-low impact velocity design is likewise validated on held-out data (Figure 5, Table 4). The paper's statement 'assuming that the D2P-VAE samples from the true posterior' (Section 3.1) is an unverified assumption about estimator quality, and the absence of repeated-seed or capacity-bounding experiments is a genuine correctness risk, but it is not circularity: the alternative failure mode is estimator underfitting, not a conclusion encoded into the model construction. Self-citations (refs. 20, 30, 53) supply the radiographic forward model and related inverse-ML methodology, but the identifiability result does not reduce to those citations. No equation defines the target parameters in terms of the observables by construction, and no fitted parameter is renamed as a prediction. Hence no circular step can be exhibited; the score of 1 reflects only the presence of minor, non-load-bearing self-citations for methods and forward-model details.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims depend on the CTH hydrocode faithfully implementing the Mie-Grüneisen and P-alpha models, on the synthetic radiograph model being representative of real experiments, on the uniform parameter prior, and on the VAE approximating the true posterior. None of these are independently verified by the paper; the noise model constants are not even given numerical values.

free parameters (4)
  • σ_blur = not specified
    Gaussian blur standard deviation in the synthetic radiograph model (Appendix A); hand-designed, numerical value not given in the manuscript, affects all training and test radiographs.
  • κ (scatter amplitude) = not specified
    Scatter scaling constant D_s = κ * G_scatter * d in Appendix A; hand-designed, value not provided.
  • a, b (background tilt coefficients) = not specified
    Coefficients of the uncorrelated tilted background scatter field B_s = ax + by in Appendix A; hand-designed, values not provided.
  • γ_g, γ_p (noise rates) = not specified
    Signal-dependent rates for gamma and photon Poisson noise in Appendix A; values not provided, yet they determine the noisy radiograph training data.
assumptions (5)
  • domain assumption CTH hydrocode correctly implements the Mie-Grüneisen EoS and P-alpha porosity models for 2024 aluminum and 4130 steel.
    Invoked throughout Section 2.1 and 4; all simulated density fields and reconstructions rely on this.
  • domain assumption The radiographic forward model (Abel transform, Beer-Lambert law, additive noise) is a faithful representation of real flyer plate radiography.
    Appendix A defines the model; the paper's claims about parameter inference from radiographs depend on this modeling choice.
  • domain assumption The conditional VAE approximates the true posterior distribution over parameters given images.
    Section 2.2 and Appendix B; the MMSE point estimates and calibration analysis rely on the VAE posterior being approximately correct.
  • ad hoc to paper Material parameters are uniformly distributed over the ranges in Table 3, representing the prior for the real experiment.
    Section 2.2; the training distribution defines the support of the learned mapping, and the paper does not justify these bounds from independent data.
  • domain assumption The density field is axially symmetric, permitting the Abel transform.
    Appendix A; stated as an assumption for the forward radiograph model.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Learning robust parameter inference and density reconstruction in flyer plate impact experiments." pith.science (2026). https://pith.science/paper/X4TBAK6D

@misc{pith2026250623914,
  author       = {Pith},
  title        = {Pith review of: Learning robust parameter inference and density reconstruction in flyer plate impact experiments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X4TBAK6D}},
  note         = {Machine review of arXiv:2506.23914}
}
read the original abstract

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

Figures

Figures reproduced from arXiv: 2506.23914 by the authors.

Figure 1
Figure 1. Overview of the proposed approach for parameter estimation and density reconstruction from radiographs acquired in flyer plate impact experiments. The radiographs-to-parameters variational autoencoder (R2P-VAE) produces distributions of physical parameters, which can be used in a hydrodynamic solver to obtain density fields and other state variables. image reconstruction (MBIR) or statistical image reconstruction (S… view at source ↗
Figure 2
Figure 2. Flyer plate experiment set-up and example impact. (a) initial geometry of the flyer plate experiment, (b) evolution of densities of the steel and aluminum over 12 µs. Densities are in units of g/cm3 . The impact shown in (b) is for the high impact velocity experiment, with vinit = 5 · 105 cm/s. Let ρ,T,P,E be the density, temperature, pressure, and internal energy of a material. These four variables define a 3/24 [… view at source ↗
Figure 3
Figure 3. Visualizations of density field and noisy radiograph for one sample of EoS and P−α parameters, using high impact velocity ((a) and (b)) and low impact velocity ((c) and (d)). To construct mappings from a space of observed density fields or radiographic images back to EoS and P−α parameters, we use a conditional variational autoencoder54, 55 (VAE) machine learning architecture. Given radiographs or density fields, th… view at source ↗
Figures from the paper (18 more)
Figure 4
Figure 4. Figure 4: Parameter estimates from the trained D2P-VAE on the testing set when the density of the 2024 aluminum from the high impact velocity experiment is used as input. For each test case, we use the mean of 1,000 posterior samples as a point estimate of the parameter values. …
Figure 5
Figure 5. Figure 5: Parameter estimates from the trained D2P-VAE on the testing set when the densities of the 2024 aluminum from the high impact velocity experiment and the low impact velocity experiment are used as input. For each test case, we use the mean of 1,000 posterior samples as …
Figure 6
Figure 6. Figure 6: Line-outs of the density field, clean radiograph, and noisy radiograph for the high impact velocity experiment of one test case. The green vertical line located at r = 0.125cm in subfigures (a), (b), and (c) indicates the location of the line-outs shown in subfigures (…
Figure 7
Figure 7. Figure 7: Line-outs of the density field, clean radiograph, and noisy radiograph for high impact velocity experiments generated with ranges of cs and s values. Note that the ranges of the parameter values shown here are substantially exaggerated compared to the ranges used elsew…
Figure 8
Figure 8. Figure 8: Representative posterior distribution predicted by the trained R2P-VAE on the testing set. We show the distribution of 100,000 posterior samples for the test case. Histograms show the distributions of predicted parameter values and vertical lines are located at the tru…
Figure 9
Figure 9. Figure 9: Posterior calibration or empirical coverage plots for the posteriors predicted by the R2P-VAE. For every test case, we used the predicted posterior to calculate credible intervals of various widths for each marginal distribution of parameter values. The points in each …
Figure 10
Figure 10. Figure 10: Density reconstructions and associated statistics using the parameters predicted by the R2P-VAE for a representative test case (the same test case as in [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Demonstration of R2P-VAE density reconstruction pipeline for out of distribution (OOD) radiographic noise, using 1,000 posterior samples. reconstructions × 1,000 test cases) to obtain the mean errors listed under “R2P-VAE samples” in [PITH_FULL_IMAGE:figures/full_fig…
Figure 12
Figure 12. Figure 12: Density reconstructions using parameters predicted by the R2P-VAE for a test case that was generated using a mismatched EoS model. The R2P-VAE was trained using a Mie-Grüneisen EoS, while the test case uses a Sesame EoS. 1,000 samples of parameters from the R2P-VAE we…
Figure 13
Figure 13. Figure 13: Histogram of the root mean squared (RMS) distance between the density fields for the mismatched EoS test case (visualized in [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]
Figure 14
Figure 14. Figure 14: Predicted posterior distribution of parameters for the mismatched EoS test case (reconstructions in [PITH_FULL_IMAGE:figures/full_fig_p014_14.png]
Figure 15
Figure 15. Figure 15: Architecture of the proposed D2P/R2P-VAE. Inputs and components used during both training and inference are colored in blue. Inputs and components used during training only (such as the true parameters and the parameter encoder) are colored purple. Components used dur…
Figure 16
Figure 16. Figure 16: Internal structure of the convolutional blocks used in the D2P/R2P-VAE and R2D-Net. The D2P/R2P-VAE is a variational autoencoder that is conditioned directly on images, which are either density fields or radiographs. The images are first passed through a convolutional…
Figure 17
Figure 17. Figure 17: Parameter estimates from the trained R2P-VAE on the testing set with clean radiographs used as input. For each test case, we use the mean of 1,000 posterior samples as a point estimate of the parameter values. We also report the Pearson correlation coefficient r in ea…
Figure 18
Figure 18. Figure 18: Parameter estimates from the trained R2P-VAE on the testing set with noisy radiographs used as input. For each test case, we use the mean of 1,000 posterior samples as a point estimate of the parameter values. We also report the Pearson correlation coefficient r in ea…
Figure 19
Figure 19. Figure 19: Architecture of the R2D-Net. The internal structure of the convolutional blocks is shown in [PITH_FULL_IMAGE:figures/full_fig_p022_19.png]
Figure 20
Figure 20. Figure 20: Density reconstructions directly from noisy radiographs using the trained R2D-Net for a representative test case. The top row of figures corresponds to the high impact velocity, and the bottom row corresponds to the low impact velocity. Subfigures: (a) ground truth de…
Figure 21
Figure 21. Figure 21: Green boxes bound the regions of interest used to compute the ROI metrics in [PITH_FULL_IMAGE:figures/full_fig_p024_21.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions

    physics.comp-ph 2025-09 conditional novelty 6.0 of 10

    A causal multi-fidelity neural surrogate, anchored to a physics-based shell ODE, predicts DT interface dynamics from radiation drive and recovers the drive from as few as four time snapshots.

Reference graph

Works this paper leans on

68 extracted references · 64 canonical work pages · cited by 1 Pith paper

  1. [1]

    Jorgensen, D. G. Using geophysical logs to estimate porosity, water resistivity, and intrinsic permeability. Tech. Rep., USGPO, (1989)

    work page 1989

  2. [2]

    S., Housen, K

    Collins, G. S., Housen, K. R., Jutzi, M. & Nakamura, A. M. Planetary impact processes in porous materials. Shock. Phenom. Granul. Porous Mater. 103–136 (2019)

    work page 2019

  3. [3]

    & Dubey, D

    Sharma, A. & Dubey, D. K. Effect of porosity on shock propagation behaviour of single crystal aluminium: A molecular dynamics investigation. Mech. Mater. 177, 104535 (2023)

    work page 2023

  4. [4]

    Li, C., Hamilton, B. W. & Strachan, A. Hotspot formation due to shock-induced pore collapse in 1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazoctane (HMX): Role of pore shape and shock strength in collapse mechanism and temperature. J. Appl. Phys. 127 (2020)

    work page 2020

  5. [5]

    & Weber, S

    Tikhonchuk, V . & Weber, S. Physics of porous materials under extreme laser-generated conditions. Matter Radiat. at Extrem. 9 (2024)

    work page 2024

  6. [6]

    Housen, K. R. & Holsapple, K. A. Impact cratering on porous asteroids. Icarus 163, 102–119 (2003)

    work page 2003

  7. [7]

    Nakamura, A. M. Impact cratering on porous targets in the strength regime. Planet. Space Sci. 149, 5–13 (2017)

    work page 2017

  8. [8]

    & Hollenbach, R

    Barker, L. & Hollenbach, R. Laser interferometer for measuring high velocities of any reflecting surface. J. Appl. Phys. 43, 4669–4675 (1972)

    work page 1972

Show all 68 references
  1. [9]

    Malone, R. M. et al. Overview of the line-imaging VISAR diagnostic at the National Ignition Facility (NIF). In International Optical Design Conference, ThA5 (Optica Publishing Group, 2006)

    2006

  2. [10]

    McCoy, C. A. & Knudson, M. D. Lagrangian technique to calculate window interface velocity from shock velocity measurements: Application for quartz windows. J. Appl. Phys. 122 (2017)

    2017

  3. [11]

    Reinhart, W. D. et al. Equation of state measurements of materials using a three-stage gun to impact velocities of 11 km/s. Int. J. Impact Eng. 26, 625–637 (2001)

    2001

  4. [12]

    & Lipkin, J

    Asay, J. & Lipkin, J. A self-consistent technique for estimating the dynamic yield strength of a shock-loaded material. J. Appl. Phys. 49, 4242–4247 (1978)

    1978

  5. [13]

    & Asay, J

    Lipkin, J. & Asay, J. Reshock and release of shock-compressed 6061-T6 aluminum. J. applied physics 48, 182–189 (1977)

    1977

  6. [14]

    & Ding, J

    Brown, J., Alexander, C., Asay, J., V ogler, T. & Ding, J. Extracting strength from high pressure ramp-release experiments. J. Appl. Phys. 114 (2013)

    2013

  7. [15]

    Williams, B. et al. Combining experimental data and computer simulations, with an application to flyer plate experiments. Bayesian Analysis 1, 765–792 (2006). 15/24

    2006

  8. [16]

    F., Blewett, P

    Barnes, J. F., Blewett, P. J., McQueen, R. G., Meyer, K. A. & Venable, D. Taylor instability in solids.J. Appl. Phys. 45, 727–732 (1974)

    1974

  9. [17]

    & Weber, S

    Colvin, J., Legrand, M., Remington, B., Schurtz, G. & Weber, S. A model for instability growth in accelerated solid metals. J. Appl. Phys. 93, 5287–5301 (2003)

    2003

  10. [18]

    Barton, N. R. et al. A multiscale strength model for extreme loading conditions. J. Appl. Phys. 109 (2011)

    2011

  11. [19]

    Miller, D. J. et al. Hugoniot measurements utilizing in situ synchrotron x-ray radiation. J. Dyn. Behav. Mater. 5, 93–104 (2019)

    2019

  12. [20]

    Serino, D. A. et al. Learning physical unknowns from hydrodynamic shock and material interface features in ICF capsule implosions. arXiv preprint arXiv:2412.20192 (2024)

    2024 arXiv

  13. [21]

    Ravishankar, S., Ye, J. C. & Fessler, J. A. Image reconstruction: From sparsity to data-adaptive methods and machine learning. Proc. IEEE 108, 86–109 (2019)

    2019

  14. [22]

    Fessler, J. A. Model-based image reconstruction for MRI. IEEE Signal Process. Mag. 27, 81–89 (2010)

    2010

  15. [23]

    Elbakri, I. A. & Fessler, J. A. Statistical image reconstruction for polyenergetic x-ray computed tomography. IEEE Transactions on Med. Imaging 21, 89–99 (2002)

    2002

  16. [24]

    Espy, M. et al. Spectral characterization of flash and high flux x-ray radiographic sources with a magnetic compton spectrometer. Rev. Sci. Instruments 92 (2021)

    2021

  17. [25]

    Espy, M. A. et al. A wide-acceptance compton spectrometer for spectral characterization of a medical x-ray source. In Medical Imaging 2016: Physics of Medical Imaging , vol. 9783, 1304–1314 (SPIE, 2016)

    2016

  18. [26]

    & Bouman, C

    Sauer, K. & Bouman, C. A local update strategy for iterative reconstruction from projections. IEEE Transactions on Signal Process. 41, 534–548 (2002)

    2002

  19. [27]

    T., Klasky, M

    McCann, M. T., Klasky, M. L., Schei, J. L. & Ravishankar, S. Local models for scatter estimation and descattering in polyenergetic x-ray tomography. Opt. Express 29, 29423–29438 (2021)

    2021

  20. [28]

    T., Hu, J

    Xu, X., Klasky, M., McCann, M. T., Hu, J. & Fessler, J. A. Swap-net: A memory-efficient 2.5d network for sparse-view 3d cone beam CT reconstruction to ICF applications. IEEE Transactions on Comput. Imaging (2025)

    2025

  21. [29]

    & Ravishankar, S

    Huang, Z., Klasky, M., Wilcox, T. & Ravishankar, S. Physics-driven learning of wasserstein GAN for density reconstruction in dynamic tomography. Appl. Opt. 61, 2805–2817 (2022)

    2022

  22. [30]

    A., Klasky, M

    Serino, D. A., Klasky, M. L., Nadiga, B. T., Xu, X. & Wilcox, T. Reconstructing richtmyer–meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks. Opt. Express 32, 43366–43386 (2024)

    2024

  23. [31]

    Hossain, M. et al. High-precision inversion of dynamic radiography using hydrodynamic features. Opt. Express 30, 14432–14452 (2022)

    2022

  24. [32]

    L., Fessler, J

    Lahiri, A., Maliakal, G., Klasky, M. L., Fessler, J. A. & Ravishankar, S. Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data. IEEE Transactions on Comput. Imaging 9, 13–28 (2023)

    2023

  25. [33]

    Wood, J. C. et al. Ultrafast imaging of laser driven shock waves using betatron x-rays from a laser wakefield accelerator. Sci. Reports 8, 11010 (2018)

    2018

  26. [34]

    Bayesian inference in physics

    von Toussaint, U. Bayesian inference in physics. Rev. Mod. Phys. 83, 943–999, DOI: 10.1103/RevModPhys.83.943 (2011)

    2011 doi

  27. [35]

    & Grédiac, M

    Pierron, F. & Grédiac, M. Towards material testing 2.0. a review of test design for identification of constitutive parameters from full-field measurements. Strain 57, e12370 (2021)

    2021

  28. [36]

    & Delikta¸ s, B

    Tariq, A. & Delikta¸ s, B. An inverse parameter identification in finite element problems using machine learning-aided optimization framework. Exp. Mech. 1–25 (2025)

    2025

  29. [37]

    & Fernandes, J

    Prates, P., Pereira, A., Sakharova, N., Oliveira, M. & Fernandes, J. Inverse strategies for identifying the parameters of constitutive laws of metal sheets. Adv. Mater. Sci. Eng. 2016, 4152963 (2016)

    2016

  30. [38]

    & Notta-Cuvier, D

    Markiewicz, É., Langrand, B. & Notta-Cuvier, D. A review of characterisation and parameters identification of materials constitutive and damage models: From normalised direct approach to most advanced inverse problem resolution. Int. J. Impact Eng. 110, 371–381 (2017)

    2017

  31. [39]

    & Vacher, P

    Pottier, T., Toussaint, F., Louche, H. & Vacher, P. Inelastic heat fraction estimation from two successive mechanical and thermal analyses and full-field measurements. Eur. J. Mech. 38, 1–11 (2013). 16/24

    2013

  32. [40]

    & Pompoli, F

    Bonfiglio, P. & Pompoli, F. Inversion problems for determining physical parameters of porous materials: Overview and comparison between different methods. Acta Acustica United with Acustica 99, 341–351 (2013)

    2013

  33. [41]

    M., Carroll, M

    Butcher, B. M., Carroll, M. M. & Holt, A. C. Shock-wave compaction of porous aluminum. J. Appl. Phys. 45, 3864–3875, DOI: 10.1063/1.1663877 (1974). https://pubs.aip.org/aip/jap/article-pdf/45/9/3864/18368181/3864_1_online.pdf

    1974 doi

  34. [42]

    Drumheller, D. S. The theoretical treatment of a porous solid using a mixture theory. Int. J. Solids Struct. 14, 441–456, DOI: https://doi.org/10.1016/0020-7683(78)90009-4 (1978)

    1978 doi

  35. [43]

    Taylor, P. A. An introduction to plastic yield strength modeling for CTH applications. Tech. Rep., Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) (2006)

    2006

  36. [44]

    CTH equation of state package: Porosity and reactive burn models

    Kerley, G. CTH equation of state package: Porosity and reactive burn models. Sandia Natl. Lab. report SAND92-0553 (1992)

    1992

  37. [45]

    H., McQueen, R

    Rice, M. H., McQueen, R. G. & Walsh, J. M. Compression of solids by strong shock waves. In Seitz, F. & Turnbull, D. (eds.) Advances in Research and Applications , vol. 6 of Solid State Physics , 1–63, DOI: https://doi.org/10.1016/ S0081-1947(08)60724-9 (Academic Press, 1958)

  38. [46]

    Constitutive Equation for the Dynamic Compaction of Ductile Porous Materials

    Herrmann, W. Constitutive Equation for the Dynamic Compaction of Ductile Porous Materials. J. Appl. Phys. 40, 2490–2499, DOI: 10.1063/1.1658021 (1969)

    1969 doi

  39. [47]

    & Holt, A

    Carroll, M. & Holt, A. C. Suggested modification of the p- α model for porous materials. J. Appl. Phys. 43, 759–761 (1972)

    1972

  40. [48]

    & Kober, E

    Menikoff, R. & Kober, E. Equation of state and Hugoniot locus for porous materials: P- α model revisited. In AIP conference proceedings, vol. 505, 129–132 (American Institute of Physics, 2000)

    2000

  41. [49]

    & Lee, T.-W

    Mahon, K. & Lee, T.-W. Compaction of granular HMX: P-α porosity model in CTH hydrocode. AIP Adv. 5 (2015)

    2015

  42. [50]

    & Michel, P

    Jutzi, M., Benz, W. & Michel, P. Numerical simulations of impacts involving porous bodies: I. implementing sub-resolution porosity in a 3d sph hydrocode. Icarus 198, 242–255 (2008)

    2008

  43. [51]

    & Davidsson, B

    de Niem, D., Kührt, E., Hviid, S. & Davidsson, B. Low velocity collisions of porous planetesimals in the early solar system. Icarus 301, 196–218 (2018)

    2018

  44. [52]

    & Wen, H

    Wang, Z. & Wen, H. A modified p-α equation of state for concrete-like materials. J. Build. Eng. 67, 106017 (2023)

    2023

  45. [53]

    A., Klasky, M

    Serino, D. A., Klasky, M. L., Nadiga, B. T., Xu, X. & Wilcox, T. Reconstructing richtmyer-meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks. Opt. Express 32, 43366–43386, DOI: 10.1364/OE.538495 (2024)

    2024 doi

  46. [54]

    Kingma, D. P. & Welling, M. Auto-encoding variational bayes. In Bengio, Y . & LeCun, Y . (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings (2014)

    2014

  47. [55]

    P., Mohamed, S., Jimenez Rezende, D

    Kingma, D. P., Mohamed, S., Jimenez Rezende, D. & Welling, M. Semi-supervised learning with deep generative models. Adv. neural information processing systems 27 (2014)

    2014

  48. [56]

    & Levine, S

    Rybkin, O., Daniilidis, K. & Levine, S. Simple and effective vae training with calibrated decoders. In International conference on machine learning, 9179–9189 (PMLR, 2021)

    2021

  49. [57]

    McHardy, J. D. An introduction to the theory and use of SESAME equations of state. Tech. Rep., Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) (2018). DOI: 10.2172/1487368

    2018 doi

  50. [58]

    Lucas, J., Tucker, G., Grosse, R. B. & Norouzi, M. Understanding posterior collapse in generative latent variable models. In Deep Generative Models for Highly Structured Data, ICLR 2019 Workshop, New Orleans, Louisiana, United States, May 6, 2019 (OpenReview.net, 2019)

    2019

  51. [59]

    Higgins, I. et al. beta-V AE: Learning basic visual concepts with a constrained variational framework. InInternational Conference on Learning Representations (2017)

    2017

  52. [60]

    & Lempitsky, V

    Ulyanov, D., Vedaldi, A. & Lempitsky, V . Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)

    2016 arXiv

  53. [61]

    Ramachandran, P., Zoph, B. & Le, Q. V . Searching for activation functions. In 6th International Conference on Learning Representations, ICLR 2018, V ancouver , BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings (OpenReview.net, 2018)

    2018

  54. [62]

    & Hutter, F

    Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. InInternational Conference on Learning Representations (2019). 17/24

    2019

  55. [63]

    Pytorch: An imperative style, high-performance deep learning library

    Paszke, A. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)

    2019 arXiv

  56. [64]

    & The PyTorch Lightning team

    Falcon, W. & The PyTorch Lightning team. PyTorch Lightning, DOI: 10.5281/zenodo.3828935 (2019)

    2019 doi

  57. [65]

    Welford, B. P. Note on a method for calculating corrected sums of squares and products. Technometrics 4, 419–420 (1962)

    1962

  58. [66]

    & Brox, T

    Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 , 234...

    2015

  59. [67]

    & Simonyan, K

    Brock, A., Donahue, J. & Simonyan, K. Large scale GAN training for high fidelity natural image synthesis. InInternational Conference on Learning Representations (2019)

    2019

  60. [68]

    posterior collapse

    Whang, J. et al. Deblurring via stochastic refinement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16293–16303 (2022). Acknowledgments This work was supported by the Laboratory Directed Research and Development program at Los Alamos Na...

    2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.