Search NASA⌕ Search

SEARCH · Search NASA

Results for “randomized methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as $J(t)$, from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green–Kubo formulas associated with various transport coefficients, including gradient flows, particle–particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived $J(t)$ and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Long-Term Impact of Endometrial Cancer Diagnosis and Treatment on Health-Related Quality of Life and Cancer Survivorship: Results From the Randomized PORTEC-2 Trial

Purpose: To evaluate the long-term health-related quality of life (HRQL) after external beam radiation therapy (EBRT) or vaginal brachytherapy (VBT) among PORTEC-2 trial patients, evaluate long-term bowel and bladder symptoms, and assess the impact of cancer on these endometrial cancer (EC) survivors. Patients and Methods: In the PORTEC-2 trial, 427 patients with stage I high–intermediate-risk EC were randomly allocated to EBRT or VBT. The 7- and 10-year HRQL questionnaires consisted of EORTC QLQ-C30; subscales for bowel and bladder symptoms; the Impact of Cancer Questionnaire; and 14 questions on comorbidities, walking aids, and incontinence pads. Analysis was done using linear mixed models for subscales and (ordinal) logistic regression with random effects for single items. A two-sided P value <.01 was considered statistically significant. Results: Longitudinal HRQL analysis showed persisting higher rates of bowel symptoms with EBRT, without significant differences in global health or any of the functioning scales. At 7 years, clinically relevant fecal leakage was reported by 10.6% in the EBRT group, versus 1.8% for VBT (P=.03), diarrhea by 8.4% versus 0.9% (P=.04), limitations due to bowel symptoms by 10.5% versus 1.8% (P=.001), and bowel urgency by 23.3% versus 6.6% (P<.001). Urinary urgency was reported by 39.3% of EBRT patients, 25.5% for VBT, P=.05. No difference in sexual activity was seen between treatment arms. Long-term impact of cancer scores was higher among the patients who had an EC recurrence or second cancer. Conclusions: More than 7 years after treatment, EBRT patients reported more bowel symptoms with impact on daily activities, and a trend for more urinary symptoms, without impact on overall quality of life or difference in cancer survivorship issues.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Simulating Fragmentation and Fluid-Induced Fracture in Disordered Media Using Random Finite-Element Meshes

Fracture and fragmentation are extremely nonlinear multiscale processes in which microscale damage mechanisms emerge at the macroscale as new fracture surfaces. Numerous numerical methods have been developed for simulating fracture initiation, propagation, and coalescence. In this paper, we present a computational approach for modeling pervasive fracture in quasi-brittle materials based on random close-packed Voronoi tessellations. Each Voronoi cell is formulated as a polyhedral finite element containing an arbitrary number of vertices and faces. Fracture surfaces are allowed to nucleate only at the intercell faces. Cohesive softening tractions are applied to new fracture surfaces in order to model the energy dissipated during fracture growth. The randomly seeded Voronoi cells provide a regularized discrete random network for representing fracture surfaces. The potential crack paths within the random network are viewed as instances of realizable crack paths within the continuum material. Mesh convergence of fracture simulations is viewed in a weak, or distributional, sense. The explicit facet representation of fractures within this approach is advantageous for modeling contact on new fracture surfaces and fluid flow within the evolving fracture network. Finally, applications of interest include fracture and fragmentation in quasi-brittle materials and geomechanical applications such as hydraulic fracturing, engineered geothermal systems, compressed-air energy storage, and carbon sequestration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Effect of liraglutide on myocardial glucose uptake and blood flow in stable chronic heart failure patients: A double-blind, randomized, placebo-controlled LIVE sub-study

Background: The glucagon-like peptide-1 analog liraglutide increases heart rate and may be associated with more cardiac events in chronic heart failure (CHF) patients. We studied whether this could be ascribed to effects on myocardial glucose uptake (MGU), myocardial blood flow (MBF) and MBF reserve (MFR). Methods and Results: CHF patients with left ventricular ejection fraction ≤45% and without type 2 diabetes were randomized to liraglutide (N = 18) 1.8 mg once daily or placebo (N = 18) for 24 weeks in a double-blinded design. Changes in MGU during an oral glucose tolerance test (OGTT) and changes in MBF and MFR from baseline to follow-up were measured quantitatively by {sup 18}F-FDG and {sup 15}O-H{sub 2}O positron emission tomography. Compared with placebo, liraglutide reduced weight (P = 0.03), HbA1c (P = 0.03) and the 2-hour glucose value during the OGTT (P = 0.004). Despite this, changes in MGU (P = 0.98), MBF (P = 0.76) and MFR (P = 0.89) from baseline to follow-up did not differ between groups. Furthermore, there was no association between the level of insulin resistance at baseline and changes in MGU in patients treated with liraglutide. Conclusion: Liraglutide did not affect MGU, MBF, or MFR in non-diabetic CHF patients. Any potential increase in cardiac events in these patients seems not to involve changes in MGU, MBF, or MFR. Trial Registration: Trial registry: http://www.ClinicalTrials.org . Identifier: NCT01472640. Url: https://clinicaltrials.gov/ct2/show/NCT01472640?term=NCT01472640&rank=1.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Computing the QRPA level density with the finite amplitude method

Here, we describe a new algorithm to calculate the vibrational nuclear level density of an atomic nucleus. Fictitious perturbation operators that probe the response of the system are generated by drawing their matrix elements from some probability distribution function. We use the Finite Amplitude Method to explicitly compute the response for each such sample. With the help of the Kernel Polynomial Method, we build an estimator of the vibrational level density and provide the upper bound of the relative error in the limit of infinitely many random samples. The new algorithm can give accurate estimates of the vibrational level density. Since it is based on drawing multiple samples of perturbation operators, its computational implementation is naturally parallel and scales like the number of available processing units.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Finally, numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.

42 ENGINEERING↗
DOE OSTI2016

Fabrication and characterization of powder metallurgy tantalum components prepared by high compaction pressure technique

The present study has investigated the consolidation behaviors of tantalum powders during compaction and sintering, and the characteristics of sintered components. For die compaction, the densification behaviors of the powders are simulated by finite element analyses based on the yield function proposed by Shima and Oyane. Accordingly, the green density distribution for coarser particles is predicted to be more uniform because they exhibits higher initial relative tap density owing to lower interparticle friction. It is also found that cold isostatic pressing is capable of producing higher dense compacts compared to the die pressing. However, unlike the compaction behavior, the sintered density of smaller particles is found to be higher than those of coarser ones owing to their higher specific surface area. The maximum sintered density was found to be 0.96 of theoretical density where smaller particles were pressed isostatically at 400 MPa followed by sintering at 2000 °C. Moreover, the effects of processing conditions on grain size and texture were also investigated. The average grain size of the sintered specimen is 30.29 μm and its texture is less than 2 times random intensity. Consequently, it is concluded that the higher pressure compaction technique is beneficial to produce high dense and texture-free tantalum components compared to hot pressing and spark plasma sintering. - Highlights: • Higher Ta density is obtained from higher pressure and sintering temperature. • High compaction method enables P/M Ta to achieve the density of 16.00 g·cm{sup −3}. • A P/M Ta component with fine microstructure and random orientation is developed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Variational Bayesian strategies for high-dimensional, stochastic design problems

This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints. We propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks. To that end, we propose a Variational Bayesian (VB) formulation and an iterative VB–Expectation-Maximization scheme that is capable of identifying a local maximum as well as a low-dimensional set of directions in the design space, along which, the objective exhibits the largest sensitivity. We demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables. In all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less. The accuracy of the approximations provided is assessed by information-theoretic metrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Safety and Short-Term Efficacy of Irreversible Electroporation and Allogenic Natural Killer Cell Immunotherapy Combination in the Treatment of Patients with Unresectable Primary Liver Cancer

Purpose: This study aimed to investigate the safety and short-term efficacy of irreversible electroporation (IRE) combined with allogenic natural killer (NK) cell immunotherapy in the treatment of patients with unresectable primary liver cancer. Materials and Methods: Between October 2015 and December 2016, 40 patients were enrolled and randomly allocated to either the IRE group (n = 22) or the IRE–NK group (n = 18). All adverse events experienced by the patients were recorded; the changes in tumor biomarkers [AFP, CA 19-9, circulating tumor cells (CTCs)], lymphocyte number and function, quality of life, clinical response, progression-free survival (PFS) and overall survival (OS) were assessed. Results: Patients who received combination therapy exhibited significantly longer median PFS and OS than who just received IRE (PFS 15.1 vs. 10.6 months, P < 0.05, OS 17.9 vs. 23.2 months, P < 0.05). The combination therapy of IRE and NK cell immunotherapy significantly reduced CTCs and increased immune function and Karnofsky performance status. Conclusion: Our data suggest a novel, promising combination therapy using IRE and allogenic NK cell immunotherapy. Larger clinical trials are required to confirm these conclusions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Correlation functions from a unified variational principle: Trial Lie groups

Time-dependent expectation values and correlation functions for many-body quantum systems are evaluated by means of a unified variational principle. It optimizes a generating functional depending on sources associated with the observables of interest. It is built by imposing through Lagrange multipliers constraints that account for the initial state (at equilibrium or off equilibrium) and for the backward Heisenberg evolution of the observables. The trial objects are respectively akin to a density operator and to an operator involving the observables of interest and the sources. We work out here the case where trial spaces constitute Lie groups. This choice reduces the original degrees of freedom to those of the underlying Lie algebra, consisting of simple observables; the resulting objects are labeled by the indices of a basis of this algebra. Explicit results are obtained by expanding in powers of the sources. Zeroth and first orders provide thermodynamic quantities and expectation values in the form of mean-field approximations, with dynamical equations having a classical Lie–Poisson structure. At second order, the variational expression for two-time correlation functions separates–as does its exact counterpart–the approximate dynamics of the observables from the approximate correlations in the initial state. Two building blocks are involved: (i) a commutation matrix which stems from the structure constants of the Lie algebra; and (ii) the second-derivative matrix of a free-energy function. The diagonalization of both matrices, required for practical calculations, is worked out, in a way analogous to the standard RPA. The ensuing structure of the variational formulae is the same as for a system of non-interacting bosons (or of harmonic oscillators) plus, at non-zero temperature, classical Gaussian variables. This property is explained by mapping the original Lie algebra onto a simpler Lie algebra. The results, valid for any trial Lie group, fulfill consistency properties and encompass several special cases: linear responses, static and time-dependent fluctuations, zero- and high-temperature limits, static and dynamic stability of small deviations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2016

Feasibility and Initial Dosimetric Findings for a Randomized Trial Using Dose-Painted Multiparametric Magnetic Resonance Imaging–Defined Targets in Prostate Cancer

Purpose: To compare dosimetric characteristics with multiparametric magnetic resonance imaging–identified imaging tumor volume (gross tumor volume, GTV), prostate clinical target volume and planning target volume, and organs at risk (OARs) for 2 treatment techniques representing 2 arms of an institutional phase 3 randomized trial of hypofractionated external beam image guided highly targeted radiation therapy. Methods and Materials: Group 1 (n=20) patients were treated before the trial inception with the standard dose prescription. Each patient had an additional treatment plan generated per the experimental arm. A total of 40 treatment plans were compared (20 plans for each technique). Group 2 (n=15) consists of patients currently accrued to the hypofractionated external beam image guided highly targeted radiation therapy trial. Plans were created as per the treatment arm, with additional plans for 5 of the group 2 experimental arm with a 3-mm expansion in the imaging GTV. Results: For all plans in both patient groups, planning target volume coverage ranged from 95% to 100%; GTV coverage of 89.3 Gy for the experimental treatment plans ranged from 95.2% to 99.8%. For both groups 1 and 2, the percent volumes of rectum/anus and bladder receiving 40 Gy, 65 Gy, and 80 Gy were smaller in the experimental plans than in the standard plans. The percent volume at 1 Gy per fraction and 1.625 Gy per fraction were compared between the standard and the experimental arms, and these were found to be equivalent. Conclusions: The dose per fraction to the OARs can be made equal even when giving a large simultaneous integrated boost to the GTV. The data suggest that a GTV margin may be added without significant dose effects on the OARs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

A Comparative Evaluation of Normal Tissue Doses for Patients Receiving Radiation Therapy for Hodgkin Lymphoma on the Childhood Cancer Survivor Study and Recent Children's Oncology Group Trials

Purpose: Survivors of pediatric Hodgkin lymphoma (HL) are recognized to have an increased risk of delayed adverse health outcomes related to radiation therapy (RT). However, the necessary latency required to observe these late effects means that the estimated risks apply to outdated treatments. We sought to compare the normal tissue dose received by children treated for HL and enrolled in the Childhood Cancer Survivor Study (CCSS) (diagnosed 1970-1986) with that of patients treated in recent Children's Oncology Group (COG) trials (enrolled 2002-2012). Methods and Materials: RT planning data were obtained for 50 HL survivors randomly sampled from the CCSS cohort and applied to computed tomography planning data sets to reconstruct the normal tissue dosimetry. For comparison, the normal tissue dosimetry data were obtained for all 191 patients with full computed tomography–based volumetric RT planning on COG protocols AHOD0031 and AHOD0831. Results: For early-stage patients, the mean female breast dose in the COG patients was on average 83.5% lower than that for CCSS patients, with an absolute reduction of 15.5 Gy. For advanced-stage patients, the mean breast dose was decreased on average by 70% (11.6 Gy average absolute dose reduction). The mean heart dose decreased on average by 22.9 Gy (68.6%) and 17.6 Gy (56.8%) for early- and advanced-stage patients, respectively. All dose comparisons for breast, heart, lung, and thyroid were significantly lower for patients in the COG trials than for the CCSS participants. Reductions in the prescribed dose were a major contributor to these dose reductions. Conclusions: These are the first data quantifying the significant reduction in the normal tissue dose using actual, rather than hypothetical, treatment plans for children with HL. These findings provide useful information when counseling families regarding the risks of contemporary RT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Cost Analysis of Complex Radiation Therapy for Patients With Head and Neck Cancer

Purpose: This cost analysis aimed to prospectively assess differences in costs between TomoTherapy and volumetric modulated arc therapy (VMAT) in patients with head and neck cancer. Methods and Materials: Economic data were gathered from a multicenter study. However, randomization was not possible due to the availability of equipment. Costs were calculated using the microcosting technique from the hospital's perspective (in 2013 euros), and the time horizon was radiation therapy. Only resources that entered the hospital production process and which were likely to vary between the strategies being compared were considered. Acute adverse events observed within the time horizon were also assessed. Results: The cost analysis was based on a total of 173 patient treatments given between 2010 and 2012 in 14 French cancer centers: 73 patients were treated with TomoTherapy, 92 with VMAT RapidArc, and 8 with VMAT SmartArc. Estimated costs of SmartArc were removed from the comparison due to the small sample size. The mean ± SD cost per patient of the treatment planning phase was €314 (±€214) for TomoTherapy and €511 (±€590) for RapidArc. Mean costs ± SD per patient of irradiation reached €3144 (±€565) for TomoTherapy and €1350 (±€299) for RapidArc. The most sensitive parameter of irradiation was the annual operating time of accelerators. Ninety-five percent confidence intervals for the mean costs of irradiation were €3016 to €3272 for TomoTherapy and €1281 to €1408 for RapidArc. The number of acute adverse events during radiation therapy was not significantly different between strategies. Conclusions: TomoTherapy appeared to be more expensive than RapidArc mainly due to the higher price of the accelerator, the higher costs of maintenance, and the longer duration of treatment sessions. Because strategies were not significantly different in clinical effect, RapidArc appeared to be the strategy to be recommended at this stage of knowledge.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Calibration of linear contact stiffnesses in discrete element models using a hybrid analytical-computational framework

Efficient selections of particle-scale contact parameters in discrete element modelling remain an open question. The purpose of this study is to provide a hybrid calibration framework to estimate linear contact stiffnesses (normal and tangential) for both two-dimensional and three-dimensional simulations. Analytical formulas linking macroscopic parameters (Young's modulus, Poisson's ratio) to mesoscopic particle parameters for granular systems are derived based on statistically isotropic packings under small-strain isotropic stress conditions. By taking the derived analytical solutions as initial approximations, the gradient descent algorithm automatically obtains a reliable numerical estimation. The introduced framework is validated with several numerical cases including randomly distributed monodisperse and polydisperse packings. The results show that this hybrid method practically reduces the time for artificial trials and errors to obtain reasonable stiffness parameters. The proposed framework can also be extended to other parameter calibration problems in DEM.

Calibration method↗
DOE OSTI2020

Detecting and tracking drift in quantum information processors

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

97 MATHEMATICS AND COMPUTING↗