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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bit error protection in cache memories

A computing device having a cache memory that is configured in a write-back mode is described. A cache controller in the cache memory acquires, from a record of bit errors that are present in each of a plurality of portions of the cache memory, a number of bit errors in a portion of the cache memory. The cache controller detects a coherency state of data stored in the portion of the cache memory. Based on the coherency state and the number of bit errors, the cache controller selects an error protection from among a plurality of error protections. The cache controller uses the selected error protection to protect the data stored in the portion of the cache memory from errors.

Kalamatianos, John↗
DOE OSTI2017

Error Convergence Characterization for Stochastic Transport Methods

There has been increased interest in reliably and efficiently solving engineering systems involving uncertainty in recent years. Characterization of the convergence of errors within the problem, including statistical and bias errors, increases confidence that chosen solution methods are appropriate for a particular problem and quantity of interest (QoI), that implementation errors have not been made, and can aid users or algorithms in solving problems within a tolerance efficiently. We demonstrate a method for characterizing error convergence in a simple stochastic transport problem. Though the approach is applicable to problems with more complicated physics, our simple problem enables analytic QoI solutions so that error convergence behavior and the identification of limiting errors can be presented with high precision. A one-dimensional, two-material, neutral-particle, and absorption-only transport problem involving uniformly distributed uncertain total cross sections is presented. Random sampling (RS) and stochastic collocation (SC) are used as stochastic solvers which sample from the uncertain space and Monte Carlo (MC) transport is employed to solve transport results on the resulting realizations. Probability density functions (PDFs) are created using either RS solutions or samples taken from a polynomial chaos expansion (PCE) constructed with SC solutions. The convergence of each solver is characterized with respect to the QoI against analytic solutions, convergence behavior is used to efficiently solve a QoI within a tolerance, and convergence plots are used to identify dominant solution errors.

42 ENGINEERING↗
DOE OSTI2024

Error mitigation, optimization, and extrapolation on a trapped-ion testbed

Current noisy intermediate-scale quantum (NISQ) trapped-ion devices are subject to errors which can significantly impact the accuracy of calculations if left unchecked. A form of error mitigation called zero noise extrapolation (ZNE) can decrease an algorithm’s sensitivity to these errors without increasing the number of required qubits. Here we explore different methods for integrating this error mitigation technique into the Variational Quantum Eigensolver (VQE) algorithm for calculating the ground state of the HeH + molecule at 0.8 Å in the presence of experimental noise. Using the Quantum Scientific Computing Open User Testbed (QSCOUT) trapped-ion device, we test three methods of scaling noise for extrapolation: time stretching the two-qubit gates, scaling the sideband detuning parameter, and inserting two-qubit gate identity operations into the ansatz circuit. We find that time stretching and sideband detuning scaling fail to scale the noise on our particular hardware in a way that can be extrapolated to zero noise. Scaling our noise with global gate identity insertions and extrapolating after variational optimization, we achieve error suppression of 96.8%, resulting in an energy estimate within –0.004 ± 0.04 hartree of the ground state energy. This is an improvement, but still outside the chemical accuracy threshold of 0.0016 hartree. Furthermore, our results show that the efficacy of this error mitigation technique depends on choosing the correct implementation for a given device architecture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Embedded Model Error Representation for Bayesian Model Calibration

Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy, is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters,within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors’ contributions to predictive uncertainty. The framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Embedded Model Error Representation for Bayesian Model Calibration

Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy,is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters, within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors' contributions to predictive uncertainty. As a result, the framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations

Projection-based reduced-order models (pROMs) show great promise as a means to accelerate many-query applications such as forward error propagation, solving inverse problems, and design optimization. In order to deploy pROMs in the context of high-consequence decision making, accurate error estimates are required to determine the region(s) of applicability in the parameter space. The following paper considers the dual-weighted residual (DWR) error estimate for pROMs and compares it to another promising pROM error estimate, machine learned error models (MLEM). Here, we show how DWR can be applied to ROMs and then evaluate DWR on two partial differential equations (PDEs): a two-dimensional linear convection–reaction–diffusion equation, and a three-dimensional static hyper-elastic beam. It is shown that DWR is able to estimate errors for pROMs extrapolating outside of their training set while MLEM is best suited for pROMs used to interpolate within the pROM training set.

42 ENGINEERING↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Minimising the error in eigenvalue calculations involving the Boltzmann transport equation using goal-based adaptivity on unstructured meshes

This article presents a method for goal-based anisotropic adaptive methods for the finite element method applied to the Boltzmann transport equation. The neutron multiplication factor, k{sub eff}, is used as the goal of the adaptive procedure. The anisotropic adaptive algorithm requires error measures for k{sub eff} with directional dependence. General error estimators are derived for any given functional of the flux and applied to k{sub eff} to acquire the driving force for the adaptive procedure. The error estimators require the solution of an appropriately formed dual equation. Forward and dual error indicators are calculated by weighting the Hessian of each solution with the dual and forward residual respectively. The Hessian is used as an approximation of the interpolation error in the solution which gives rise to the directional dependence. The two indicators are combined to form a single error metric that is used to adapt the finite element mesh. The residual is approximated using a novel technique arising from the sub-grid scale finite element discretisation. Two adaptive routes are demonstrated: (i) a single mesh is used to solve all energy groups, and (ii) a different mesh is used to solve each energy group. The second method aims to capture the benefit from representing the flux from each energy group on a specifically optimised mesh. The k{sub eff} goal-based adaptive method was applied to three examples which illustrate the superior accuracy in criticality problems that can be obtained.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

A review of setup error in supine breast radiotherapy using cone-beam computed tomography

Setup error in breast radiotherapy (RT) measured with 3-dimensional cone-beam computed tomography (CBCT) is becoming more common. The purpose of this study is to review the literature relating to the magnitude of setup error in breast RT measured with CBCT. The different methods of image registration between CBCT and planning computed tomography (CT) scan were also explored. A literature search, not limited by date, was conducted using Medline and Google Scholar with the following key words: breast cancer, RT, setup error, and CBCT. This review includes studies that reported on systematic and random errors, and the methods used when registering CBCT scans with planning CT scan. A total of 11 relevant studies were identified for inclusion in this review. The average magnitude of error is generally less than 5 mm across a number of studies reviewed. The common registration methods used when registering CBCT scans with planning CT scan are based on bony anatomy, soft tissue, and surgical clips. No clear relationships between the setup errors detected and methods of registration were observed from this review. Further studies are needed to assess the benefit of CBCT over electronic portal image, as CBCT remains unproven to be of wide benefit in breast RT.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness

We report the accuracy of deep learning, i.e., deep neural networks, can be characterized by dividing the total error into three main types: approximation error, optimization error, and generalization error. Whereas there are some satisfactory answers to the problems of approximation and optimization, much less is known about the theory of generalization. Most existing theoretical works for generalization fail to explain the performance of neural networks in practice. To derive a meaningful bound, we study the generalization error of neural networks for classification problems in terms of data distribution and neural network smoothness. We introduce the cover complexity (CC) to measure the difficulty of learning a data set and the inverse of the modulus of continuity to quantify neural network smoothness. A quantitative bound for expected accuracy/error is derived by considering both the CC and neural network smoothness. Although most of the analysis is general and not specific to neural networks, we validate our theoretical assumptions and results numerically for neural networks by several data sets of images. The numerical results confirm that the expected error of trained networks scaled with the square root of the number of classes has a linear relationship with respect to the CC. We also observe a clear consistency between test loss and neural network smoothness during the training process. In addition, we demonstrate empirically that the neural network smoothness decreases when the network size increases whereas the smoothness is insensitive to training dataset size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning for continuous quantum error correction on superconducting qubits

Abstract Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Error field measurements with rotating RMP fields for DIII-D H-mode

3D magnetic sensors are employed to identify the amplitude and toroidal phase of error fields (EF) by analyzing the torque balance for magnetic islands entrained by rotating resonant magnetic perturbations (RMPs) in DIII-D H-mode plasmas. This technique of torque balance allows for efficient error field identification, offering a valuable tool for scenario-specific and optimized error field compensation (EFC) and requires only magnetic diagnostics. The torque balance used in this work includes the contribution from electromagnetic torque due to error fields, wall response, RMP fields, and viscous. Results show that viscous torque plays a crucial role, particularly during locked modes and H-mode plasmas, ensuring accurate data fits with lower residuals. The torque balance technique reveals that the L- and H-mode plasmas have distinct EF configurations, and consistent with a model-based EF analysis including MHD response in IPEC and the SURFMN EF simulation. This technique shows great robustness in measuring the intrinsic EF amplitude regardless of its amplitude or toroidal phase. Repeated discharges with EFC disparities exhibit consistent results of intrinsic error field within a reasonable range near the “standard” error field compensation. Additionally, the use of a rotating n = 1 resonant magnetic perturbation offers the advantage of reducing disruption risks by entraining saturated magnetic islands. These findings are instrumental for optimizing EF correction in fusion devices, thereby enhancing tearing mode suppression and overall plasma stability.

3D magnetic sensors↗
DOE OSTI2023

Unified analysis of finite-size error for periodic Hartree-Fock and second order Møller-Plesset perturbation theory

Despite decades of practice, finite-size errors in many widely used electronic structure theories for periodic systems remain poorly understood. For periodic systems using a general Monkhorst-Pack grid, there has been no comprehensive and rigorous analysis of the finite-size error in the Hartree-Fock theory (HF) and the second order Møller-Plesset perturbation theory (MP2), which are the simplest wavefunction based method, and the simplest post-Hartree-Fock method, respectively. Such calculations can be viewed as a multi-dimensional integral discretized with certain trapezoidal rules. Due to the Coulomb singularity, the integrand has many points of discontinuity in general, and standard error analysis based on the Euler-Maclaurin formula gives overly pessimistic results. The lack of analytic understanding of finite-size errors also impedes the development of effective finite-size correction schemes. We propose a unified analysis to obtain sharp convergence rates of finite-size errors for the periodic HF and MP2 theories. Our main technical advancement is a generalization of the result of Lyness [Math. Comp. 30 (1976), pp. 1–23] for obtaining sharp convergence rates of the trapezoidal rule for a class of non-smooth integrands. Our result is applicable to three-dimensional bulk systems as well as low dimensional systems (such as nanowires and 2D materials). Our unified analysis also allows us to prove the effectiveness of the Madelung-constant correction to the Fock exchange energy, and the effectiveness of a recently proposed staggered mesh method for periodic MP2 calculations (see X. Xing, X. Li, and L. Lin [J. Chem. Theory Comput. 17 (2021), pp. 4733–4745]). In conclusion, our analysis connects the effectiveness of the staggered mesh method with integrands with removable singularities, and suggests a new staggered mesh method for reducing finite-size errors of periodic HF calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Perturbative Stability and Error-Correction Thresholds of Quantum Codes

Topologically ordered phases are stable to local perturbations, and topological quantum error-correcting codes enjoy thresholds to local errors. We connect the two notions of stability by constructing classical statistical mechanics models for decoding general Calderbank-Shor-Steane codes and classical linear codes. Our construction encodes correction success probabilities under uncorrelated bit-flip and phase-flip errors, and simultaneously describes a generalized ℤ 2 lattice-gauge theory with quenched disorder. We observe that the clean limit of the latter is precisely the discretized imaginary-time path integral of the corresponding quantum code Hamiltonian when the errors are turned into a perturbative 𝑋 or 𝑍 magnetic field. Motivated by error-correction considerations, we define general order parameters for all such generalized ℤ 2 lattice-gauge theories, and show that they are generally lower bounded by success probabilities of error correction. For CSS codes satisfying the low-density parity-check condition and with a sufficiently large code distance, we prove the existence of a low-temperature ordered phase of the corresponding lattice-gauge theories, particularly for those lacking Euclidean spatial locality and/or when there is a nonzero code rate. We further argue that these results provide evidence for stable phases in the corresponding perturbed quantum Hamiltonians, obtained in the limit of continuous imaginary time. To do so, we distinguish space- and timelike defects in the lattice-gauge theory. A high free-energy cost of spacelike defects corresponds to a successful “memory experiment” and suppresses the energy splitting among the ground states, while a high free-energy cost of timelike defects corresponds to a successful “stability experiment” and points to a nonzero gap to local excitations.

quantum error correction↗
DOE OSTI2022

ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis

Nanometer circuits are increasingly prone to timing errors, escalating the need for fault injection frameworks to accurately evaluate their impact on applications. Here in this paper, we propose ARETE, a novel cross-layer, fault-injection framework that combines dynamic-binary instrumentation with machine learning-guided dynamic-timing analysis. ARETE enables accurate fault-injection into any application by estimating the location of the injecting errors via dynamic-timing analysis. To accelerate fault-injection, we develop a novel, data-aware, machine learning-based mechanism that dynamically pre-selects the error-prone instructions and limits the application of the costly dynamic-timing analysis only to them. To evaluate ARETE's accuracy, our fully automated toolflow is configured to support fault-injection based on detailed post-layout gate-level simulations as well as via existing workload-agnostic error models. Our results for various workloads, including an autonomous-driving library, show that the location and time of injected errors performed by ARETE, is 89.9% consistent with fault-injection based on full gate-level simulation. On average, ARETE executes 84.6x faster than gate-level simulation and at a cost of 3.4% loss in the program output quality estimation. When compared to the existing statistical fault-injection tools that are based on workload-agnostic error models, ARETE improves the accuracy of fault-injection rate and output quality estimation by 143.9% and 40.4% on average, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Efficient Lossy Compression for Scientific Data Based on Pointwise Relative Error Bound

An effective data compressor is becoming increasingly critical to today's scientific research, and many lossy compressors are developed in the context of absolute error bounds. Based on physical/chemical definitions of simulation fields or multiresolution demand, however, many scientific applications need to compress the data with a pointwise relative error bound (i.e., the smaller the data value, the smaller the compression error to tolerate). To this end, we propose two optimized lossy compression strategies under a state-of-the-art three-staged compression framework (prediction + quantization + entropy-encoding). The first strategy (called block-based strategy) splits the data set into many small blocks and computes an absolute error bound for each block, so it is particularly suitable for the data with relatively high consecutiveness in space. The second strategy (called multi-threshold-based strategy) splits the whole value range into multiple groups with exponentially increasing thresholds and performs the compression in each group separately, which is particularly suitable for the data with a relatively large value range and spiky value changes. We implement the two strategies rigorously and evaluate them comprehensively by using two scientific applications which both require lossy compression with point-wise relative error bound. Experiments show that the two strategies exhibit the best compression qualities on different types of data sets respectively. In conclusion, the compression ratio of our lossy compressor is higher than that of other state-of-the-art compressors by 17.2-618 percent on the climate simulation data and 30-210 percent on the N-body simulation data, with the same relative error bound and without degradation of the overall visualization effect of the entire data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

SU-E-T-152: Error Sensitivity and Superiority of a Protocol for 3D IMRT Quality Assurance

Purpose: To test if the parameters included in our 3D QA protocol with current tolerance levels are able to detect certain errors and show the superiority of 3D QA method over single ion chamber measurements and 2D gamma test by detecting most of the introduced errors. The 3D QA protocol parameters are: TPS and measured average dose difference, 3D gamma test with 3mmDTA/3% test parameters, and structure volume for which the TPS predicted and measured absolute dose difference is greater than 6%. Methods: Two prostate and two thoracic step-and-shoot IMRT patients were investigated. The following errors were introduced to each original treatment plan: energy switched from 6MV to 10MV, linac jaws retracted to 15cmx15cm, 1,2,3 central MLC leaf pairs retracted behind the jaws, single central MLC leaf put in or out of the treatment field, Monitor Units (MU) increased and decreased by 1 and 3%, collimator off by 5 and 15 degrees, detector shifted by 5mm to the left and right, gantry treatment angle off by 5 and 15 degrees. QA was performed on each plan using single ion chamber, 2D ion chamber array for 2D gamma analysis and using IBA's COMPASS system for 3D QA. Results: Out of the three tested QA methods single ion chamber performs the worst not detecting subtle errors. 3D QA proves to be the superior out of the three methods detecting all of introduced errors, except 10MV and 1% MU change, and MLC rotated (those errors were not detected by any QA methods tested). Conclusion: As the way radiation is delivered evolves, so must the QA. We believe a diverse set of 3D statistical parameters applied both to OAR and target plan structures provides the highest level of QA.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

SU-E-T-550: Range Effects in Proton Therapy Caused by Systematic Errors in the Stoichiometric Calibration

Purpose: The procedure for proton treatment planning involves the conversion of the patient's X-ray CT from Hounsfield units into relative stopping powers (RSP), using a stoichiometric calibration curve (Schneider 1996). In clinical practice a 3.5% margin is added to account for the range uncertainty introduced by this process and other errors. RSPs for real tissues are calculated using composition data and the Bethe-Bloch formula (ICRU 1993). The purpose of this work is to investigate the impact that systematic errors in the stoichiometric calibration have on the proton range. Methods: Seven tissue inserts of the Gammex 467 phantom were imaged using our CT scanner. Their known chemical compositions (Watanabe 1999) were then used to calculate the theoretical RSPs, using the same formula as would be used for human tissues in the stoichiometric procedure. The actual RSPs of these inserts were measured using a Bragg peak shift measurement in the proton beam at our institution. Results: The theoretical calculation of the RSP was lower than the measured RSP values, by a mean/max error of - 1.5/-3.6%. For all seven inserts the theoretical approach underestimated the RSP, with errors variable across the range of Hounsfield units. Systematic errors for lung (average of two inserts), adipose and cortical bone were - 3.0/-2.1/-0.5%, respectively. Conclusion: There is a systematic underestimation caused by the theoretical calculation of RSP; a crucial step in the stoichiometric calibration procedure. As such, we propose that proton calibration curves should be based on measured RSPs. Investigations will be made to see if the same systematic errors exist for biological tissues. The impact of these differences on the range of proton beams, for phantoms and patient scenarios, will be investigated. This project was funded equally by the Engineering and Physical Sciences Research Council (UK) and Ion Beam Applications (Louvain-La-Neuve, Belgium)

60 APPLIED LIFE SCIENCES↗