Publications

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This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

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Improvised explosive devices (IEDs) have injured and killed numerous soldiers and civilians as a consequence of military operations in the Middle East. With gaps in existing technologies, the US military required devices for quickly addressing deadly IEDs without harming military personnel or inflicting severe damage to the environment. In response to this need, Sandia developed Stingray, a clear, plastic handheld device to quickly and safely disable threatening IEDs. Stingray is designed to be used in two configurations: a coherent water blade for cutting operations and as a water slug for general device disruption. Prior to disabling an IED, Explosive Ordnance Disposal (EOD) technicians will x-ray the target using tools such as Sandia's X-ray Toolkit (XTK) to determine which function operators should use to dismantle the IED. For example, if the operator knows exactly which wires to cut, they can use the precision water blade. If the operator wants to create a general disruption, they can use the water slug function.

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Here, the design, fabrication, and characterization of an actively tunable long-wave infrared detector, made possible through direct integration of a graphene-enabled metasurface with a conventional type-II superlattice infrared detector, are reported. This structure allows for post-fabrication tuning of the detector spectral response through voltage-induced modification of the carrier density within graphene and, therefore, its plasmonic response. These changes modify the transmittance through the metasurface, which is fabricated monolithically atop the detector, allowing for spectral control of light reaching the detector. Importantly, this structure provides a fabrication-controlled alignment of the metasurface filter to the detector pixel and is entirely solid-state. Using single pixel devices, relative changes in the spectral response exceeding 8% have been realized. These proof-of-concept devices present a path toward solid-state hyperspectral imaging with independent pixel-to-pixel spectral control through a voltage-actuated dynamic response.

Simple but mission-critical internet-based applications that require extremely high reliability and availability could potentially benefit from running on robust public programmable blockchain platforms such as Ethereum. Unfortunately, program code running on such blockchains is ordinarily publicly viewable, rendering these platforms unsuitable for applications requiring strict privacy of application code, data, and results. However, might it be possible to encode an application's business logic and data for these platforms in such a way that it becomes impossible for unauthorized parties to infer any meaningful information whatsoever about the semantics of the data, and the operations being performed on that data? In this report, we describe GABLE (Garbled Autonomous Bots Leveraging Ethereum), a system concept developed at Sandia that achieves this security goal in a limited, but still useful range of circumstances. GABLE, uses simple but effective algorithms to permit secure private execution of garbled state machines (and more efficient garbled circuits) on public computing resources. We give an example working implementation for garbled state machines, written using the Python and Solidity programming languages, and outline how our methods can be extended to support a more powerful garbled universal circuit model of computation. The capability embodied by the GABLE, system has significant potential applications, a few of which we discuss in this report.

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

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Sandia’s Z Pulsed Power Facility is able to dynamically compress matter to extreme states with exceptional uniformity, duration, and size, which are ideal for investigating fundamental material properties of high energy density conditions. X-ray diffraction (XRD) is a key atomic scale probe since it provides direct observation of the compression and strain of the crystal lattice and is used to detect, identify, and quantify phase transitions. Because of the destructive nature of Z-Dynamic Material Property (DMP) experiments and low signal vs background emission levels of XRD, it is very challenging to detect a diffraction signal close to the Z-DMP load and to recover the data. We have developed a new Spherical Crystal Diffraction Imager (SCDI) diagnostic to relay and image the diffracted x-ray pattern away from the load debris field. The SCDI diagnostic utilizes the Z-Beamlet laser to generate 6.2-keV Mn–He_α x rays to probe a shock-compressed material on the Z-DMP load. Finally, a spherically bent crystal composed of highly oriented pyrolytic graphite is used to collect and focus the diffracted x rays into a 1-in. thick tungsten housing, where an image plate is used to record the data.

Bayesian optimization (BO) is an effective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity models, many realistic applications provide multiple levels of fidelity with various levels of computational complexity and predictive capability. In this work, we propose a multi-fidelity Bayesian optimization method for design applications with both known and unknown constraints. The proposed framework, called sMF-BO-2CoGP, is built on a multi-level CoKriging method to predict the objective function. An external binary classifier, which we approximate using a separate CoKriging model, is used to distinguish between feasible and infeasible regions. Finally, the sMF-BO-2CoGP method is demonstrated using a series of analytical examples and a flip-chip application for design optimization to minimize the deformation due to warping under thermal loading conditions.