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DOE OSTI2020

Stochastic gradient descent algorithm for stochastic optimization in solving analytic continuation problems

We propose a stochastic gradient descent based optimization algorithm to solve the analytic continuation problem in which we extract real frequency spectra from imaginary time Quantum Monte Carlo data. The procedure of analytic continuation is an ill-posed inverse problem which is usually solved by regularized optimization methods, such like the Maximum Entropy method, or stochastic optimization methods. The main contribution of this work is to improve the performance of stochastic optimization approaches by introducing a supervised stochastic gradient descent algorithm to solve a flipped inverse system which processes the random solutions obtained by a type of Fast and Efficient Stochastic Optimization Method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Estimating basis functions in massive fields under the spatial mixed effects model

Abstract Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Pazdernik, Karl↗

DOE OSTI2020

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1985

Advanced reliability methods for structural evaluation

Fast probability integration (FPI) methods, which can yield approximate solutions to such general structural reliability problems as the computation of the probabilities of complicated functions of random variables, are known to require one-tenth the computer time of Monte Carlo methods for a probability level of 0.001; lower probabilities yield even more dramatic differences. A strategy is presented in which a computer routine is run k times with selected perturbed values of the variables to obtain k solutions for a response variable Y. An approximating polynomial is fit to the k 'data' sets, and FPI methods are employed for this explicit form.

Wirsching, P. H.↗

NASA NTRS2014

Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

CFD↗

NASA NTRS2006

Application of the Bootstrap Statistical Method in Deriving Vibroacoustic Specifications

This paper discusses the Bootstrap Method for specification of vibroacoustic test specifications. Vibroacoustic test specifications are necessary to properly accept or qualify a spacecraft and its components for the expected acoustic, random vibration and shock environments seen on an expendable launch vehicle. Traditionally, NASA and the U.S. Air Force have employed methods of Normal Tolerance Limits to derive these test levels based upon the amount of data available, and the probability and confidence levels desired. The Normal Tolerance Limit method contains inherent assumptions about the distribution of the data. The Bootstrap is a distribution-free statistical subsampling method which uses the measured data themselves to establish estimates of statistical measures of random sources. This is achieved through the computation of large numbers of Bootstrap replicates of a data measure of interest and the use of these replicates to derive test levels consistent with the probability and confidence desired. The comparison of the results of these two methods is illustrated via an example utilizing actual spacecraft vibroacoustic data.

Hughes, William O.↗

NASA NTRS2010

Estimation of Signal Coherence Threshold and Concealed Spectral Lines Applied to Detection of Turbofan Engine Combustion Noise

Combustion noise from turbofan engines has become important, as the noise from sources like the fan and jet are reduced. An aligned and un-aligned coherence technique has been developed to determine a threshold level for the coherence and thereby help to separate the coherent combustion noise source from other noise sources measured with far-field microphones. This method is compared with a statistics based coherence threshold estimation method. In addition, the un-aligned coherence procedure at the same time also reveals periodicities, spectral lines, and undamped sinusoids hidden by broadband turbofan engine noise. In calculating the coherence threshold using a statistical method, one may use either the number of independent records or a larger number corresponding to the number of overlapped records used to create the average. Using data from a turbofan engine and a simulation this paper shows that applying the Fisher z-transform to the un-aligned coherence can aid in making the proper selection of samples and produce a reasonable statistics based coherence threshold. Examples are presented showing that the underlying tonal and coherent broad band structure which is buried under random broadband noise and jet noise can be determined. The method also shows the possible presence of indirect combustion noise. Copyright 2011 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America.

Miles, Jeffrey Hilton↗

NASA NTRS2011

Post-Fragmentation Whole Genome Amplification-Based Method

This innovation is derived from a proprietary amplification scheme that is based upon random fragmentation of the genome into a series of short, overlapping templates. The resulting shorter DNA strands (<400 bp) constitute a library of DNA fragments with defined 3 and 5 termini. Specific primers to these termini are then used to isothermally amplify this library into potentially unlimited quantities that can be used immediately for multiple downstream applications including gel eletrophoresis, quantitative polymerase chain reaction (QPCR), comparative genomic hybridization microarray, SNP analysis, and sequencing. The standard reaction can be performed with minimal hands-on time, and can produce amplified DNA in as little as three hours. Post-fragmentation whole genome amplification-based technology provides a robust and accurate method of amplifying femtogram levels of starting material into microgram yields with no detectable allele bias. The amplified DNA also facilitates the preservation of samples (spacecraft samples) by amplifying scarce amounts of template DNA into microgram concentrations in just a few hours. Based on further optimization of this technology, this could be a feasible technology to use in sample preservation for potential future sample return missions. The research and technology development described here can be pivotal in dealing with backward/forward biological contamination from planetary missions. Such efforts rely heavily on an increasing understanding of the burden and diversity of microorganisms present on spacecraft surfaces throughout assembly and testing. The development and implementation of these technologies could significantly improve the comprehensiveness and resolving power of spacecraft-associated microbial population censuses, and are important to the continued evolution and advancement of planetary protection capabilities. Current molecular procedures for assaying spacecraft-associated microbial burden and diversity have inherent sample loss issues at practically every step, particularly nucleic acid extraction. In engineering a molecular means of amplifying nucleic acids directly from single cells in their native state within the sample matrix, this innovation has circumvented entirely the need for DNA extraction regimes in the sample processing scheme.

Benardini, James↗

DOE OSTI2020

First-principles study on the mechanical and thermodynamic properties of MoNbTaTiW

Refractory high-entropy alloys (RHEAs) are emerging as new materials for high temperature structural applications because of their stable mechanical and thermal properties at temperatures higher than 2273 K. In this study, the mechanical properties of MoNbTaTiW REDEA are examined by applying calculations based on first-principles density functional theory (DFT) and using a large unit cell with 100 randomized atoms. The phase calculation of MoNbTaTiW with CALPHAD method shows the existence of a stable body-centered cubic structure at a high temperature and a hexagonal closely packed phase at a low temperature. The predicted phase, shear modulus, Young’s modulus, Poisson’s ratio, and hardness values are consistent with available experimental results. The linear thermal expansion coefficient, vibrational entropy, and vibrational heat capacity of MoNbTaTiW RHEA are investigated in accordance with Debye-Grüneisen theory. These results may provide a basis for future research related to the application of RHEAs.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Block copolymer self-assembly to pattern gold nanodots for site-specific placement of DNA origami and attachment of nanomaterials

Directed placement of DNA origami could play a key role in future integrated nanoelectronic devices. Here we demonstrated the site-selective attachment of DNA origami on gold dots formed using a pattern transfer method through block copolymer self-assembly. First, a random copolymer brush layer is grafted on the Si surface and then poly (styrene-b-methylmethacrylate) block copolymer is spin-coated to give a hexagonal nanoarray after annealing. UV irradiation followed by acetic acid etching is used to remove the PMMA, creating cylindrical holes and then oxygen plasma etching removes the random copolymer layer inside those holes. Next, metal evaporation, followed by lift-off creates a gold dot array. We evaluated different ligand functionalization of Au dots, as well as DNA hybridization to attach DNA origami to the nanodots. DNA-coated Au nanorods are assembled on the DNA origami as a step towards creating nanowires and to facilitate electron microscopy characterization of the attachment of DNA origami on these Au nanodots. The DNA hybridization approach showed better DNA attachment to Au nanodots than localization by electrostatic interaction. Here, this work contributes to the understanding of DNA-templated assembly, nanomaterials, and block copolymer nanolithography. Furthermore, the work shows potential for creating DNA-templated nanodevices and their placement in ordered arrays in future nanoelectronics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

Monte Carlo evaluation of the semiclassical multi-step direct reaction series

We show how the multi-step direct reaction series can be evaluated using Monte Carlo methods. Nucleon-nucleon collisions occur according to the random selection of a nucleon’s attenuation factor along its classical trajectory. The particles and hole excited in a collision are selected from a local Fermi distribution. We assume that the particles continue to propagate and possibly collide again before leaving the nucleus. We assume that holes collide in place, to possibly produce other particles and holes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

An investigation of high entropy alloy conductivity using first-principles calculations

The Kubo–Greenwood equation, in combination with the first-principles Korringa–Kohn–Rostoker coherent potential approximation (KKR-CPA) can be used to calculate the DC residual resistivity of random alloys at T = 0 K. We implemented this method in a multiple scattering theory based ab initio package, MuST, and applied it to the ab initio study of the residual resistivity of the high entropy alloy Al x CoCrFeNi as a function of x. The calculated resistivities are compared with experimental data. We also predict the residual resistivity of refractory high entropy alloy MoNbTaV x W. Furthermore, the calculated resistivity trends are also explained using theoretical arguments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis

Multi-dimensional spectral-imaging is a mainstay of the scanning probe and electron microscopies, micro-Raman, and various forms of chemical imaging. In many cases, individual spectra can be fit to a specific functional form, with the model parameter maps, providing direct insight into material properties. Since spectra are often acquired across a spatial grid of points, spatially adjacent spectra are likely to be similar to one another; yet, this fact is almost never used when considering parameter estimation for functional fits. On datasets tried here, we show that by utilizing proximal information, whether it be in the spatial or spectral domains, it is possible to improve the reliability and increase the speed of such functional fits by ~2-3x, as compared to random priors. We explore and compare three distinct new methods: (1) spatially averaging neighborhood spectra, and propagating priors based on functional fits to the averaged case, (2) hierarchical clustering-based methods where spectra are grouped hierarchically based on response, with the priors propagated progressively down the hierarchy, and (3) regular clustering without hierarchical methods with priors propagated from fits to cluster means. Our results highlight that utilizing spatial and spectral neighborhood information is often critical for accurate parameter estimation in noisy environments, which we show for ferroelectric hysteresis loops acquired on a prototypical PbTiO3 thin film with piezoresponse spectroscopy. This method is general and applicable to any spatially measured spectra where functional forms are available. Examples include exploring the superconducting gap with tunneling spectroscopy, using the Dynes formula, or current-voltage curve fits in conductive atomic force microscopy mapping. Here we explore the problem for ferroelectric hysteresis, which, given its large parameter space, constitutes a more difficult task than, for example, fitting current-voltage curves with a Schottky emission formula.

42 ENGINEERING↗

DOE OSTI2020

Survey of Image Denoising Methods for Medical Image Classification

Medical imaging devices, such as X-ray machines, inherently produce images that suffer from visual noise. Our objectives were to (i.) determine the effect of image denoising on a medical image classification task, and (ii.) determine if there exists a correlation between image denoising performance and medical image classification performance. We performed the medical image classification task on chest X-rays using the DenseNet-121 convolutional neural network (CNN) and used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics as the image denoising performance measures. We first found that different denoising methods can make a statistically significant difference in classification performance for select labels. We also found that denoising methods affect fine-tuned models more than randomly-initialized models and that fine-tuned models have significantly higher and more uniform performance than randomly-initialized models. Lastly, we found that there is no significant correlation between PSNR and SSIM values and classification performance for our task.

Michael, Peter↗

DOE OSTI2022

An improved method to access initial states in relativistic heavy-ion collisions

Abstract Observables in heavy-ion collisions are generally categorized into centralities, which reflect an average over events within a range of impact parameters including a wide variety of initial-state configurations. A multiple binning method using spectator neutrons within each centrality has been shown to provide access to events with rare initial-state conditions. This work suggests an improvement in quantifying the difference between standard centrality and spectator neutron binning towards accessing the initial-state properties. A selection of events with higher initial-state density at a fixed participating nucleon number was observed to result in larger final-state particle production and smaller elliptic flow. The relative difference between observables in centrality and spectator binning shows reduced sensitivity for the observables dominated by impact parameter fluctuations in the initial state, such as triangular flow. This property renders the spectator binning method a good candidate for separating geometric contributions from random fluctuations in the initial state towards final-state observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Position Papers for the ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems

At the request of the Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR), this program committee has been tasked with organizing a workshop to identify basic research needs in cybersecurity and privacy to better support DOE's science and energy mission. As part of the process, the program committee is soliciting community input in the form of position papers to help identify significant use cases, facility issues, and other barriers to enabling verifiably trustworthy computational science while preserving data confidentiality as appropriate for scientific workflows of interest to DOE. The program committee will review these position papers and based on the fit of their area of expertise and interest, selected contributors will have the opportunity to participate in the workshop currently planned as a virtual event November 3-5th, 2021. The thrust areas that will be explored by this workshop are the following: (1) Algorithms for secure, scalable, privacy-enhancing technologies and frameworks, including: Federated AI/ML, Differential privacy, Randomized algorithms, Adversarial modeling & simulation, Graph algorithms, and Formal methods; (2) Platforms to support the entire scientific-computing ecosystem, including edge computing for large-scale experiments, focusing on heterogeneous systems and distributed systems, including: Heterogeneous computing systems, Distributed computing systems, and Secure data architectures; and (3) Data workflows to allow agile use of data while preserving integrity and privacy, making the important properties verifiable either at runtime or post-computation, including: Integrity and provenance and Data management infrastructure. Topics that are out-of-scope for the workshop include discussing specific proposed solutions or areas that are clearly out of DOE's fundamental and applied-sciences mission scope, e.g., cryptography, enterprise security, and general-operations technology.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗