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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

SDSS-IV MaStar: Stellar parameter determination with continuum-supplemented full-spectrum fitting

We present a stellar parameter catalog built to accompany the MaStar Stellar Library, which is a comprehensive collection of empirical, medium-resolution stellar spectra. We constructed this parameter catalog by using a multicomponent χ 2 fitting approach to match MaStar spectra to models generated by interpolating the ATLAS9-based BOSZ model spectra. The total χ 2 for a given model is defined as the sum of components constructed to characterize narrow-band features of observed spectra (e.g., absorption lines) and the broadband continuum shape separately. Extinction and systematics due to flux calibration were taken into account in the fitting. The χ 2 distribution for a given region of model space was sampled using a Markov chain Monte Carlo (MCMC) algorithm, the data from which were then used to extract atmospheric parameter estimates (T eff , log g, [Fe/H], and [α/Fe]), their corresponding uncertainties, and direct extinction measurements. Two methods were used to extract parameters and uncertainties: one that accepts the MCMC’s prescribed minimum-χ 2 result, and one that uses Bayesian inference to compute a likelihood-weighted mean from the χ 2 distribution sampled by the MCMC. Results were evaluated for internal consistency using repeat observations where available and by comparing them with external data sets (e.g., APOGEE-2 and Gaia DR2). Our spectral-fitting exercise reveals possible deficiencies in current theoretical model spectra, illustrating the potential power of MaStar spectra for helping to improve the models. This paper represents an update to the parameters that were originally presented with SDSS-IV DR17. The MaStar parameter catalog containing our BestFit results is available on the SDSS-IV DR17 website as part of version 2 of the MaStar stellar parameter value-added catalog.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A comparison of Gaussian processes and neural networks for computer model emulation and calibration

The Department of Energy relies on complex physics simulations for prediction in domains like cosmology, nuclear theory, and materials science. These simulations are often extremely computationally intensive, with some requiring days or weeks for a single simulation. In order to assure their accuracy, these models are calibrated against observational data in order to estimate inputs and systematic biases. Because of their great computational complexity, this process typically requires the construction of an emulator, a fast approximation to the simulation. In this paper, two emulator approaches are compared: Gaussian process regression and neural networks. Their emulation accuracy and calibration performance on three real problems of Department of Energy interest is considered. On these problems, the Gaussian process emulator tends to be more accurate with narrower, but still well-calibrated uncertainty estimates. The neural network emulator is accurate, but tends to have large uncertainty on its predictions. Finally, as a result, calibration with the Gaussian process emulator produces more constrained posteriors that still perform well in prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

Abstract Background The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. Results Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Conclusions Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Aghdam, Rosa↗
DOE OSTI2022

Proxy-Based Bayesian Inversion Of Poroelastic Simulations To Interpret Strain Tensor Data Measured During Well Testing

The long runtimes of 3D poroelastic numerical simulators makes it impractical to interpret deformation datasets using many inversion schemes. Recent advances in instrumentation have made it possible to measure the strain tensor during well testing, but the lack of robust inversion methods is limiting the ability to interpret these data. We have developed an inversion workflow that reduces the number of computations required to complete a Bayesian inversion using DREAMzs. The workflow trains a KNN model using output from the poroelastic simulator, and then uses the KNN model as a proxy for the simulator during inversion. The workflow also includes a strategy for ensuring the results from the proxy model converge to the results from the simulator, ensuring the accuracy of the final results. An idealized example configured to represent a well test in a deep aquifer is used to verify that the workflow correctly identifies parameters and characterizes noise. Field data measured using strainmeters during an injection test at an oil reservoir in Oklahoma are used to evaluate performance with a real dataset. The workflow identified 265 history matching solutions out of 1240 total simulation runs (21% acceptance ratio), and the results are used to characterize posterior parameter distribution and evaluate the prediction uncertainty. This approach makes it feasible to invert strain data measured during well testing and this has the potential to improve the characterization of aquifers and reservoirs.

Roudini, Soheil↗
NASA NTRS1987

Isotropic probability measures in infinite dimensional spaces: Inverse problems/prior information/stochastic inversion

Let R be the real numbers, R(n) the linear space of all real n-tuples, and R(infinity) the linear space of all infinite real sequences x = (x sub 1, x sub 2,...). Let P sub n :R(infinity) approaches R(n) be the projection operator with P sub n (x) = (x sub 1,...,x sub n). Let p(infinity) be a probability measure on the smallest sigma-ring of subsets of R(infinity) which includes all of the cylinder sets P sub n(-1) (B sub n), where B sub n is an arbitrary Borel subset of R(n). Let p sub n be the marginal distribution of p(infinity) on R(n), so p sub n(B sub n) = p(infinity)(P sub n to the -1(B sub n)) for each B sub n. A measure on R(n) is isotropic if it is invariant under all orthogonal transformations of R(n). All members of the set of all isotropic probability distributions on R(n) are described. The result calls into question both stochastic inversion and Bayesian inference, as currently used in many geophysical inverse problems.

Backus, George↗
NASA NTRS1987

Isotropic probability measures in infinite-dimensional spaces

Let R be the real numbers, R(n) the linear space of all real n-tuples, and R(infinity) the linear space of all infinite real sequences x = (x sub 1, x sub 2,...). Let P sub in :R(infinity) approaches R(n) be the projection operator with P sub n (x) = (x sub 1,...,x sub n). Let p(infinity) be a probability measure on the smallest sigma-ring of subsets of R(infinity) which includes all of the cylinder sets P sub n(-1) (B sub n), where B sub n is an arbitrary Borel subset of R(n). Let p sub n be the marginal distribution of p(infinity) on R(n), so p sub n(B sub n) = p(infinity) (P sub n to the -1 (B sub n)) for each B sub n. A measure on R(n) is isotropic if it is invariant under all orthogonal transformations of R(n). All members of the set of all isotropic probability distributions on R(n) are described. The result calls into question both stochastic inversion and Bayesian inference, as currently used in many geophysical inverse problems.

Backus, George↗
DOE OSTI2012

Material Aging and Degradation Detection and Remaining Life Assessment for Plant Life Management

One of the major factors that may impact long term operations is structural material degradation, Detecting materials degradation, estimating the remaining useful life (RUL) of the component, and determining approaches to mitigating the degradation are important from the perspective of long term operations. In this study, multiple nondestructive measurement and monitoring methods were evaluated for their ability to assess the material degradation state. Metrics quantifying the level of damage from these measurements were defined, and evaluated for their ability to provide estimates of remaining life of the component. An example of estimating the RUL from nondestructive measurements of material degradation condition is provided.

Bayesian prognostics↗
DOE OSTI2025

Enhanced signal of momentum broadening in hard splittings for $γ$-tagged jets in a multistage approach

We investigate medium-induced modifications to jet substructure observables that characterize hard splitting patterns in central Pb-Pb collisions at the top energy of the Large Hadron Collider (LHC). Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $γ$-tagged jets. The results show that quark jets undergo a non-monotonic modification compared to gluon jets in observables such as the Pb-Pb to $p$-$p$ ratio of the Soft Drop prong angle $r_g$, the relative prong transverse momentum $k_{T,g}$ and the groomed mass $m_g$ distributions. Due to this non-monotonic modification, $γ$-tagged jets, enriched in quark jets, provide surprisingly clear signals of medium-induced structural modifications, distinct from effects dominated by selection bias. This work highlights the potential of hard substructures in $γ$-tagged jets as powerful tools for probing the jet-medium interactions in high-energy heavy-ion collisions. All simulations for $γ$-tagged jet analyses carried out in this paper used triggered events containing at least one hard photon, which highlights the utility of these observables for future Bayesian analysis.

FOS: Physical sciences↗
DOE OSTI2012

Quantitative Phenomena Identification and Ranking Table (QPIRT) for Bayesian uncertainty quantification

Propagating parameter uncertainty for a nuclear reactor system code is a challenging problem due to often non-linear system response to the numerous parameters involved and lengthy computational times; issues that compound when a statistical sampling procedure is adopted, since the code must be run many times. The number of parameters sampled must therefore be limited to as few as possible that still accurately characterize the uncertainty in the system response. A Quantitative Phenomena Identification and Ranking Table (QPIRT) was developed to accomplish this goal. The QPIRT consists of two steps: a 'Top-Down' step focusing on identifying the dominant physical phenomena controlling the system response, and a 'Bottom-Up' step which focuses on determining the correlations from those key physical phenomena that significantly contribute to the response uncertainty. The Top-Down step evaluates phenomena using the governing equations of the system code at nominal parameter values, providing a 'fast' screening step. The Bottom-Up step then analyzes the correlations and models for the phenomena identified from the Top-Down step to find which parameters to sample. The QPIRT, through the Top-Down and Bottom-Up steps thus provides a systematic approach to determining the limited set of physically relevant parameters that influence the uncertainty of the system response. This strategy was demonstrated through an application to the RELAP5-based analysis of a PWR Total Loss of main Feedwater Flow (TLOFW) accident, also known as feed and bleed' scenario, . Ultimately, this work is the first component in a larger task of building a calibrated uncertainty propagation framework. The QPIRT is an essential piece because the uncertainty of those selected parameters will be calibrated to data from both Separate and Integral Effect Tests (SETs and IETs). Therefore the system response uncertainty will incorporate the knowledge gained from the database of past large IETs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

High-Temperature Sapphire Pressure Sensors for Harsh Environments

The project aimed to develop and characterize an optical pressure sensor capable of withstanding high temperatures, up to 1000 °C. Survivability in harsh environments was enabled using sapphire for both the substrate and the optical fibers, therefore mitigating thermal expansion mismatch. Such material cannot be machined through conventional microfabrication techniques, and unique additive and subtractive methods had to be studied and implemented. Subtractive methods included laser-machining the sapphire substrate using a picosecond laser machining tool. At first, the parameters of importance were theoretically and experimentally evaluated such that the fluence, the pulse spacing, and the number of passes were found to strongly influence the laser ablation process. A simulation tool was designed to predict the final pattern when inputting a set of parameters and a profile. This model was complemented with laser ablation material physics models and Bayesian uncertainty analysis for one dimensional and three dimensional ablation processes. An additive process called thermocompression bonding was studied in order to seal the diaphragm substrate to the back cavity. At first, bonding strength using this technique was examined using the chevron-notch method that allow to determine the fracture toughness of this bond. This parameter is independent of the geometry used and therefore can be extended to other geometries used in actual sensor bonding. A theoretical and experimental study of internal damage caused by the laser micromachining of the sapphire material was carried out. The research identified enhanced strength and fracture toughness along laser ablated surfaces. This was determined using bend bar specimens and Vicker’s microindentations. Softening of the plasticity behavior (as indicated by nanoindentation experiments) was also investigated using finite deformation plasticity modeling via a contact mechanics finite element simulation. Good correlations between experiments and the model were achieved to determine material parameters that can be used in future MEMS based sensor design and modeling to minimize sensor failure loads. The enhanced strength and fracture toughness was determined to be due to compressive residual stress near laser ablated surfaces. This was confirmed through x-ray spectra taken along different crystal structure planes in combination and Bayesian uncertainty analysis. The Bayesian analysis inferred the entire residual strain tensor (six components) and the residual stress tensor. The results illustrated compressive in-plane stress was the most likely cause of enhanced strength and toughness after laser machining. Due to major equipment failures, a parallel route for fabricating the sensor was taken, using commercially available sapphire removal method (subtractive) and ceramic adhesive bonding (additive). The fully-packaged sensor utilized laser micromachining for the sapphire substrate fabrication and was comprised only of high-temperature-compatible materials. Calibration tests of the sensor in both room-temperature and high-temperature plane-wave tubes showed good linearity and consistent frequency response, in the tested frequency range. The sensor also showed good thermal stability in tests up to 300 °C, proving the fiber-optic lever and its packaging process to be a promising approach for high-temperature measurements. The calibration from 23 °C to 300 °C gave a raw sensitivity of 3.9 × 10 -7 V/Pa and a normalized sensitivity of 9.8 × 10 -8 Pa -1 which were consistent within the temperature range. The sensor had a resonance at 16.3 kHz and a flat band (±3 dB) up to 10.3 kHz. The noise floor of the sensor at 1 kHz was 316 nV/Hz 1/2 , resulting in a MDP of 0.8 Pa. Previously, diaphragm delamination due to CTE mismatch was found at 500 °C, causing packaging failure. Unfortunately high temperature validation in hot jet environment could not be made in time. In the future, it is suggested that the two sapphire substrates be bonded by thermal compression bonding with an intermediate platinum layer instead of using an alumina-based adhesive, and therefore achieve higher temperature capacity up to 1000 °C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2016

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

plant communities↗
DOE OSTI2025

Resilient information and inference networks under mixed-trust sensing

With ubiquitous digitization, sensing, and computational intelligence deployed in increasingly more and broader domains, including critical infrastructure, potentially misleading and destabilizing effects of multimodal anomalies and adversarial behavior are growing in importance. Here, we develop randomized and reinforcement learning-based strategies for strategically recruiting and utilizing deployed (and, thus, vulnerable and potentially faulty and/or compromised) nodes from information and inference networks, while defending against adversaries that attempt to misguide assessments of inferred variables. Recognizing that, besides communication and other costs, sampling from any observable node can either provide true data or dangerously expose our inference to misinformation (without being easily distinguishable what actually happens), the proposed strategies proceed by progressively recruiting nodes and cautiously scaling their information contribution based on assumed, or, in our reinforcement learning approach, intelligently weighed trustworthiness, with the learning approach also considering network-wide, threat-inclusive risk/value tradeoffs. While avoiding the hardware, communication, analytical and computational burden of explicit redundancy, the proposed defensive schemes enable on-the-fly assessments of underlying processes, and system-wide situational awareness with demonstrable resilience against adversarial activities.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches

Highlights: • The KED model with road and vegetation density as covariates showed good performance for all six synoptic classes. • The model using KED demonstrated the best results (R{sup 2} = 0.92; RMSE = 3.14 ppb). • The model using OK demonstrated a R{sup 2} = 0.85 and RMSE = 4 ppb. • Our results showed that errors appear to be substantially reduced with the KED model. • When measurements at numerous monitoring stations in a region are available, it may be better to use KED. • The BME may be more useful for prediction when data is not available. Ground-level ozone (O{sub 3}) is a powerful oxidizing agent and a harmful pollutant affecting human health, forests and crops. Estimating O{sub 3} exposure is a challenge because it exhibits complex spatiotemporal patterns. The aim in this study was to provide high-resolution maps (100 × 100 m) of O{sub 3} for the metropolitan area of Montreal, Canada. We assessed the kriging with external drift (KED) model to estimate O{sub 3} concentration by synoptic weather classes for 2010. We compared these results with ordinary kriging (OK), and a simple average of 12 monitoring stations. We also compared the estimates obtained for the 2010 summer with those from a Bayesian maximum entropy (BME) model reported in the literature (Adam-Poupart et al., 2014). The KED model with road and vegetation density as covariates showed good performance for all six synoptic classes (daily R{sup 2} estimates ranging from 0.77 to 0.92 and RMSE from 2.79 to 3.37 ppb). For the summer of 2010, the model using KED demonstrated the best results (R{sup 2} = 0.92; RMSE = 3.14 ppb), followed by the OK model (R{sup 2} = 0.85, RMSE = 4 ppb). Our results showed that errors appear to be substantially reduced with the KED model. This may increase our capacity of linking O{sub 3} levels to health problems by means of improved assessments of ambient exposures. However, future work integrating the temporal dependency in the data is needed to not overstate the performance of the KED model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

A probabilistic graphical model based stochastic input model construction

Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A machine learning approach for determining temperature-dependent bandgap of metal oxides utilizing Allen–Heine–Cardona theory and O’Donnell model parameterization

To evaluate the high temperature sensing properties of metal oxide and perovskite materials suitable for use in combustion environments, it is necessary to understand the temperature dependence of their bandgaps. Although such temperature-driven changes can be calculated via the Allen–Heine–Cardona (AHC) theory, which assesses electron–phonon coupling for the bandgap correction at given temperatures, this approach is computationally demanding. Another approach to predict bandgap temperature-dependence is the O’Donnell model, which uses analytical expressions with multiple fitting parameters that require bandgap information at 0 K. This work employs data-driven Gaussian process regression (GPR) to predict the parameters employed in the O’Donnell model from a set of physical features. We use a sample of 54 metal oxides for which density functional theory has been performed to calculate the bandgap at 0 K, and the AHC calculations have been carried out to determine the shift in the bandgap at non-zero temperatures. As the AHC calculations are impractical for high-throughput screening of materials, the developed GPR model attempts to alleviate this issue by predicting the O'Donnell parameters purely from physical features. To mitigate the reliability issues arising from the very small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as quantify the uncertainty associated with the predictions. The method captures well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions of the O’Donnell parameters and, therefore, the bandgap as a function of temperature for any novel material.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Cosmic acceleration from modified gravity with Palatini formalism

We study new FRW type cosmological models of modified gravity treated on the background of Palatini approach. These models are generalization of Einstein gravity by the presence of a scalar field non-minimally coupled to the curvature. The models employ Starobinsky's term in the Lagrangian and dust matter. Therefore, as a by-product, an exhausted cosmological analysis of general relativity amended by quadratic term is presented. We investigate dynamics of our models, confront them with the currently available astrophysical data as well as against ΛCDM model. We have used the dynamical system methods in order to investigate dynamics of the models. It reveals the presence of a final sudden singularity. Fitting free parameters we have demonstrated by statistical analysis that this class of models is in a very good agreement with the data (including CMB measurements) as well as with the standard ΛCDM model predictions. One has to use statefinder diagnostic in order to discriminate among them. Therefore Bayesian methods of model selection have been employed in order to indicate preferred model. Only in the light of CMB data the concordance model remains invincible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Supercharging simulation-based inference for Bayesian optimal experimental design

Abstract Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods are able to match or outperform by up to 22% existing state-of-the-art approaches across standard BOED benchmarks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Iterative Importance Sampling Algorithms for Parameter Estimation

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is a challenging task. Several sampling algorithms have been proposed over the past years that take an iterative approach to constructing a proposal distribution. We investigate the applicability of such algorithms by applying them to two realistic and challenging test problems, one in subsurface flow, and one in combustion modeling. More specifically, we implement importance sampling algorithms that iterate over the mean and covariance matrix of Gaussian or multivariate t-proposal distributions. Our implementation leverages massively parallel computers, and we present strategies to initialize the iterations using “coarse” MCMC runs or Gaussian mixture models.

97 MATHEMATICS AND COMPUTING↗