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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Lagrangian displacement field estimators in cosmology

The late-time nonlinear Lagrangian displacement field is highly correlated with the initial field, so reconstructing it could enable us to extract primordial cosmological information. Our previous work carefully studied the displacement field reconstructed from the late-time density field using the iterative method proposed and found that it does not fully converge to the true, underlying displacement field (e.g., ~8 % offset at k ~ 0.2 h Mpc –1 at z = 0.6 ). We also constructed the Lagrangian perturbation theory model for the reconstructed field, but the model could not explain the discrepancy between the true and the reconstructed fields in the previous work. Here, the main sources of the discrepancy were speculated to be a numerical artifact in the displacement estimator due to the discreteness of the sample. In this paper, we develop two new estimators of the displacement fields to reduce such a numerical discreteness effect, the normalized momentum estimator and the rescaled resumed estimator. We show that the discrepancy reported is not due to the numerical artifacts. We conclude that the method cannot fully reconstruct the shape of the nonlinear displacement field at the redshift we studied, while it is still an efficient baryon acoustic oscillation reconstruction method. In parallel, by properly accounting for the UV-sensitive term in a reconstruction procedure with an effective field theory approach, we improve the theoretical model for the reconstructed displacement field, by almost 5 times, from ~15% to the level of a few percent at k ~ 0.2 h Mpc –1 at the redshift z = 0.6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Indigo: A Domain-Specific Language for Fast, Portable Image Reconstruction

© 2018 IEEE. Linear operators used in iterative methods like conjugate gradient have typically been implemented either as ''matrix-driven'' subroutines backed by explicit sparse or dense matrices, or as ''matrix-free'' subroutines that implement specific linear operations directly (e.g. FFTs). The matrix-driven approach is generally more portable because it can target widely-Available BLAS libraries, but it can be inefficient in terms of time and space complexity. In contrast, the matrix-free approach is more performant because it leverages structure in operations, but it requires each operator be re-implemented on each new platform. To increase performance and portability, we propose a hybrid approach that represents linear operators as expression trees. Leaf nodes in the tree are either matrix-free or matrix-driven operators, and interior nodes represent mathematical compositions (sums, products, transposes) or structural compositions (stacks, block diagonals, etc.) of the leaf operators. This representation enables expert-guided reordering and fusion transformations that can improve performance or reduce memory pressure. We implement our approach in a domain-specific language called Indigo. We assess Indigo on image reconstruction problems arising in four application areas: magnetic resonance imaging, ptychography, magnetic particle imaging, and fluorescent microscopy. We give performance results from vendor BLAS libraries, and we introduce specializations to Sparse BLAS routines that achieve near-Roofline performance on multi-core, many-core, and GPU systems.

Driscoll, Michael↗
DOE OSTI2015

Software-defined Radio Based Wireless Tomography: Experimental Demonstration and Verification

This letter presents an experimental demonstration of software-defined-radio-based wireless tomography using computer-hosted radio devices called Universal Software Radio Peripheral (USRP). This experimental brief follows our vision and previous theoretical study of wireless tomography that combines wireless communication and RF tomography to provide a novel approach to remote sensing. Automatic data acquisition is performed inside an RF anechoic chamber. Semidefinite relaxation is used for phase retrieval, and the Born iterative method is utilized for imaging the target. Experimental results are presented, validating our vision of wireless tomography.

Bonior, Jason D↗
DOE OSTI2021

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

58 GEOSCIENCES↗
DOE OSTI2020

Filtered Backprojection in Compton Imaging using a Spherical Harmonic Wiener Filter with Pixelated CdZnTe

Filtered backprojection is an image reconstruction technique for Compton imaging that provides reasonably high resolution at much lower computational costs when compared to iterative methods. Here, this work applies a Wiener filter that has been derived for spherical harmonics on Compton imaging using the OrionUM pixelated CdZnTe imaging-spectrometer. To regularize the filter, an investigation is made into the power spectral density of the signal and noise to develop an appropriate spectral signal-to-noise ratio model for the restoration process. Experimental measurements were conducted with two 228Th sources placed 30° apart. The resulting filtered image of the two sources have an average full-width-at-half-maximum (FWHM) of 9.8° or 7.5° when using a mean squared error and structural similarity optimization approach respectively; an improvement from the 29.0° FWHM image when using simple backprojection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Transmission Constraint Filtering in Large-Scale Security-Constrained Unit Commitment

When solving the security-constrained unit commitment problem, one of the most complicating factors is handling the large number of transmission constraints, corresponding to both base case and N - 1 line contingency scenarios. Although it is well known that only a few of these constraints need to be enforced, identifying this critical subset of constraints efficiently remains a challenge. In this letter, we propose a novel and simple iterative contingency-screening procedure that is able to eliminate 99.4% of the constraints selected by existing iterative methods, allowing for the solution of much larger-scale problems. We report computational results in realistic instances with up to 6468 buses and 9000 transmission lines. Furthermore, the method was also independently implemented and evaluated at MISO, where it performed faster than alternative methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Decision-Dependent Uncertainty-Aware Distribution System Planning Under Wildfire Risk

The interaction between power systems and wildfires can be dangerous and costly. Distribution grids can be liable for the outbreak of wildfires during extreme weather. In wildfire-prone areas, investment planning should consider the impact of operational actions on wildfire-related uncertainties affecting line failure likelihood. Here, in this case, endogenous-based uncertainty modeling should comprise the backbone of the investment planning model viz-a-viz the inability of standard exogenous-based uncertainty modeling. Therefore, we propose a decision-dependent uncertainty (DDU) aware methodology to optimize investment portfolios for distribution systems, considering that high power-flow levels in high-threat areas can ignite wildfires and increase line failure probability. The methodology identifies the best combination of upgrades (new lines, hardening existing lines, and placing switching devices). Methodologically, we propose a two-stage distributionally robust planning optimization problem with DDU that considers the distribution system's multiperiod operation. The first stage determines optimal switching actions and line investments, and the second stage evaluates the worst-case expected operational cost under a DDU framework designed to account for the endogenous impact of power-flow levels and hardening investment decisions in the line failure probabilities. An iterative method is tailored to handle the problem and numerical experiments demonstrate a more prepared grid to deal with wildfire risk.

Power systems investment planning↗
DOE OSTI2012

Evaluation of metal artifacts in MVCT systems using a model based correction method

Purpose: To evaluate the performance of a model based image reconstruction method in reducing metal artifacts in the megavoltage computed tomography (MVCT) images of a phantom representing bilateral hip prostheses and to compare with the filtered-backprojection (FBP) technique. Methods: An iterative maximum likelihood polychromatic algorithm for CT (IMPACT) is used with an additional model for the pair/triplet production process and the energy dependent response of the detectors. The beam spectra for an in-house bench-top and TomoTherapy Trade-Mark-Sign MVCTs are modeled for use in IMPACT. The empirical energy dependent response of detectors is calculated using a constrained optimization technique that predicts the measured attenuation of the beam by various thicknesses (0-24 cm) of solid water slabs. A cylindrical (19.1 cm diameter) plexiglass phantom containing various cylindrical inserts of relative electron densities 0.295-1.695 positioned between two steel rods (2.7 cm diameter) is scanned in the bench-top MVCT that utilizes the bremsstrahlung radiation from a 6 MeV electron beam passed through 4 cm solid water on the Varian Clinac 2300C and in the imaging beam of the TomoTherapy Trade-Mark-Sign MVCT. The FBP technique in bench-top MVCT reconstructs images from raw signal normalized to air scan and corrected for beam hardening using a uniform plexiglass cylinder (20 cm diameter). The IMPACT starts with a FBP reconstructed seed image and reconstructs the final image in 150 iterations. Results: In both MVCTs, FBP produces visible dark shading in the image connecting the steel rods. In the IMPACT reconstructed images this shading is nearly removed and the uniform background is restored. The average attenuation coefficients of the inserts and the background are very close to the corresponding values in the absence of the steel inserts. In the FBP images of the bench-top MVCT, the shading causes 4%-9.5% underestimation of electron density at the central inserts with an average of (6.3 {+-} 1.8)% for the range of electron densities studied. In the uniform plexiglass background, the shadow creates 0.8%-4.7% underestimation of electron density with an average of (2.9 {+-} 1.2)%. In the corresponding IMPACT images, the underestimation in the shaded plexiglass background is 0.3%-1.8% with an average of (0.9 {+-} 0.5)% and 1.4%-6.8% with an average of (2.8 {+-} 2.7)% in the central insert region. In the FBP images of the TomoTherapy Trade-Mark-Sign MVCT, this shading creates 2.6%-6.7% underestimation of electron density with an average of (3.7 {+-} 1.4)% at the central inserts and 5.9%-7.2% underestimation in the background with an average of (6.4 {+-} 0.5)%. In the IMPACT images, the uniform background between the steel rods is restored with 0.3%-1.0% underestimation of electron density with an average of (0.7 {+-} 0.3)%. The corresponding underestimation at the central inserts of the IMPACT images is -0.4%-0.1% with an average of (-0.1 {+-} 0.2)%. Conclusions: The shading metal artifact has been nearly removed in MVCT images using the IMPACT algorithm with the accurate geometry of the system, proper modeling of energy dependent response of detectors, and all relevant photon interaction processes. This results less than 1% difference in electron density in the background plexiglass and less than 3% averaged over the range of electron densities investigated.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Nematic order of spin structures of clusters in the hubbard model

The possibility of interpreting the normal pseudogap state of cuprates as a result of the formation of spin and charge structures is investigated for solutions of the Hubbard model of a finite 2D cluster based on the mean field method. The iterative self-consistency procedure reduces the initial uncorrelated spin distributions to stable structures. The Fourier components of the charge and spin distributions in such structures have peaks for characteristic incommensurate quasi-momenta depending on the doping. It is shown that for any doping, the density of states of the system has a sharp minimum (pseudogap) at the Fermi level. This emergence of the gap just at the Fermi level is a property typical of not only the superconducting state, but also the normal state of spin glasses. The characteristics of the Fermi surface averaged over the implemented structures and the properties of quasiparticles in the nodal and antinodal regions of the quasi-momentum are considered.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Electron-magnetohydrodynamic description of a steady-state current flow in a plasma with a three-dimensional localized density perturbation

The problem of a steady-state current flow through a localized density perturbation is analyzed in the framework of electron magnetohydrodynamics. It is examined how the degree of localization of such a flow depends on the plasma magnetization parameter and the geometric characteristics of the density perturbation. Corrections to the unperturbed current field are found by the iteration method. It is shown that the first-order correction to the current can be determined for an arbitrary perturbation. In the case of perfectly conducting plasma, the second-order correction can be found only for dipole perturbations, for which the integral change in the density is zero. In this case, the first-order correction to the current is localized, which makes possible the existence of the second-order correction. The finite plasma conductivity also assists current localization; therefore, for sufficiently small values of the plasma magnetization parameter, both first- and second-order steady-state corrections to the current can be obtained for any moderate plasma density perturbation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Improving the Communication Pattern in Matrix-Vector Operations for Large Scale-Free Graphs by Disaggregation

Matrix-vector multiplication is the key operation in any Krylov-subspace iteration method. We are interested in Krylov methods applied to problems associated with the graph Laplacian arising from large scale-free graphs. Furthermore, computations with graphs of this type on parallel distributed-memory computers are challenging. This is due to the fact that scale-free graphs have a degree distribution that follows a power law, and currently available graph partitioners are not efficient for such an irregular degree distribution. The lack of a good partitioning leads to excessive interprocessor communication requirements during every matrix-vector product. Here, we present an approach to alleviate this problem based on embedding the original irregular graph into a more regular one by disaggregating (splitting up) vertices in the original graph. The matrix-vector operations for the original graph are performed via a factored triple matrix-vector product involving the embedding graph. And even though the latter graph is larger, we are able to decrease the communication requirements considerably and improve the performance of the matrix-vector product.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

G-Mapper: Learning a Cover in the Mapper Construction

The Mapper algorithm is a visualization technique in topological data analysis (TDA) that outputs a graph reflecting the structure of a given dataset. However, the Mapper algorithm requires tuning several parameters in order to generate a “nice” Mapper graph. This paper focuses on selecting the cover parameter. We present an algorithm that optimizes the cover of a Mapper graph by splitting a cover repeatedly according to a statistical test for normality. Our algorithm is based on G-means clustering, which searches for the optimal number of clusters in 𝑘-means by iteratively applying the Anderson–Darling test. Our splitting procedure employs a Gaussian mixture model to carefully choose the cover according to the distribution of the given data. In conclusion, experiments for synthetic and real-world datasets demonstrate that our algorithm generates covers so that the Mapper graphs retain the essence of the datasets, while also running significantly faster than a previous iterative method.

G-means clustering↗
DOE OSTI2023

Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum

Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processor’s qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors, increasing the overall fidelity and hence the overall quality of circuit induction. Earlier this year, a pioneering work, namely HAMMER, published in ASPLOS-22 demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they intuitively assumed that errors occur in local clusters, and that at higher average Hamming distances this structure falls away. In this work, we show that such a correlation structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the new iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, the fidelity improvement is up to 17.8%. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to integrate and provide more reliable circuit induction results.

Stein, Samuel A.↗
DOE OSTI2014

Physics Integration KErnels (PIKE)

Pike is a software library for coupling and solving multiphysics applications. It provides basic interfaces and utilities for performing code-to-code coupling. It provides simple "black-box" Picard iteration methods for solving the coupled system of equations including Jacobi and Gauss-Seidel solvers. Pike was developed originally to couple neutronics and thermal fluids codes to simulate a light water nuclear reactor for the Consortium for Simulation of Light-water Reactors (CASL) DOE Energy Innovation Hub. The Pike library contains no physics and just provides interfaces and utilities for coupling codes. It will be released open source under a BSD license as part of the Trilinos solver framework (trilinos.org) which is also BSD. This code provides capabilities similar to other open source multiphysics coupling libraries such as LIME, AMP, and MOOSE.

Pawlowski, Roger↗
DOE OSTI2021

PNNL-CompBio/bayesian-inference

Bayesian Metabolic Inference is a platform to integrate genome-scale multi-omics data into an iterative method to obtain optimized recommendations for optimal target compound production. This method uses a variational inference approach to approximate new posterior data to analyze in order to obtain useful correlations and control coefficients.

Kumar, Neeraj↗
DOE OSTI2015

Direct radiative effect by multicomponent aerosol over China

The direct radiative effect (DRE) of multiple aerosol species (sulfate, nitrate, ammonium, black carbon (BC), organic carbon (OC), and mineral aerosol) and their spatiotemporal variations over China were investigated using a fully coupled meteorology–chemistry model (WRF-Chem) for the entire year of 2006. We made modifications to improve model performance, including updating land surface parameters, improving the calculation of transition metal-catalyzed oxidation of SO 2 , and adding in heterogeneous reactions between mineral aerosol and acid gases. The modified model well reproduced the magnitude, seasonal pattern, and spatial distribution of the measured meteorological conditions, concentrations of PM 10 and its components, and aerosol optical depth (AOD). A diagnostic iteration method was used to estimate the overall DRE of aerosols and contributions from different components. At the land surface, all kinds of aerosol species reduced the incident net radiation flux with a total DRE of 10.2 W m -2 over China. Aerosols significantly warm the atmosphere with the national mean DRE of +10.8 W m -2 . BC was the leading radiative-heating component (+8.7 W m -2 ), followed by mineral aerosol (+1.1 W m -2 ). At the top of the atmosphere (TOA), BC introduced the largest radiative perturbation (+4.5 W m -2 ), followed by sulfate (-1.4 W m -2 ). The overall perturbation of aerosols on radiation transfer is quite small over China, demonstrating the counterbalancing effect between scattering and adsorbing aerosols. Aerosol DRE at the TOA had distinct seasonality, generally with a summer maximum and winter minimum, mainly determined by mass loadings, hygroscopic growth, and incident radiation flux.

Huang, Xin↗
DOE OSTI2022

Extension of the PINN diffusion model to k-eigenvalue problems

This paper extends our recent work on the Physics-Informed Neural Networks (PINN) approach for the fixed source diffusion models and applies it to the diffusion theory based k-eigenvalue problems. To make the PINN equitable for the eigenvalue problems, we introduce a novel integral regularization term to the loss function in the framework, and allow the direct inference of the principal eigenvalue and the associated eigenfunction. The regularization term enforces a pre-defined value on the integration of the model predictions, and this value can be directly related to a physical property of the system. We also introduce an additional learnable parameter to approximate the principal eigenvalue. As a proof of principle, we solve the one-group two-dimensional k-eigenvalue neutron diffusion equation in this work. We then provide two numerical examples to demonstrate the applicability of the PINN approach. In each example, we solve the k-eigenvalue diffusion equation in a multi-region configuration constrained with a set of Robin boundary conditions for generality. We use a FEM solution based on the power-iteration method to verify the results of the PINN solution. The results showed relative percentage error in the predicted eigenvalue of about 0.77% and about 1.2% for example 1 and example 2, respectively. The mean absolute error in the predicted flux for example 1 is ∼ 0.002 and for example 2 is ∼ 0.0024. These results indicate some preliminary successes of the PINN application to k-eigenvalue problems. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram

X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for time- or dose-critical in vivo/in situ/operando (tomography) experiments because it requires only a single intensity measurement. However, the phase information of the wave field is lost during the measurement and must be recovered. Conventional algebraic and iterative methods often rely on specific approximations or boundary conditions that may not be met by many samples or experimental setups. In addition, they require manual tuning of reconstruction parameters by experts, making them less adaptable for complex or variable conditions. Here we present a self-learning approach for solving the inverse problem of phase retrieval in the near-field regime of Fresnel theory using a single intensity measurement (hologram). A physics-informed generative adversarial network is employed to reconstruct both the phase and absorbance of the unpropagated wave field in the sample plane from a single hologram. Unlike most state-of-the-art deep learning approaches for phase retrieval, our approach does not require paired, unpaired, or simulated training data. This significantly broadens the applicability of our approach, as acquiring or generating suitable training data remains a major challenge due to the wide variability in sample types and experimental configurations. The algorithm demonstrates robust and consistent performance across diverse imaging conditions and sample types, delivering quantitative, high-quality reconstructions for both simulated data and experimental datasets acquired at beamline P05 at PETRA III (DESY, Hamburg), operated by Helmholtz-Zentrum Hereon. Furthermore, it enables the simultaneous retrieval of both phase and absorption information.

36 MATERIALS SCIENCE↗