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

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

BASS: An R Package for Fitting and Performing Sensitivity Analysis of Bayesian Adaptive Spline Surfaces

In this work, we present the R package BASS as a tool for nonparametric regression. The primary focus of the package is fitting fully Bayesian adaptive spline surface (BASS) models and performing global sensitivity analyses of these models. The BASS framework is similar to that of Bayesian multivariate adaptive regression splines (BMARS) from Denison, Mallick, and Smith (1998), but with many added features. The software is built to efficiently handle significant amounts of data with many continuous or categorical predictors and with functional response. Under our Bayesian framework, most priors are automatic but these can be modified by the user to focus on parsimony and the avoidance of overfitting. If directed to do so, the software uses parallel tempering to improve the reversible jump Markov chain Monte Carlo (RJMCMC) methods used to perform inference. We discuss the implementation of these features and present the performance of BASS in a number of analyses of simulated and real data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

TBASS: A Robust Adaptation of Bayesian Adaptive Spline Surfaces

The R package TBASS is an extension of the BASS package created by Francom and Sansó (2019). The package is used to fit a Bayesian multivariate adaptive spline to a dataset that either follows a Student’s t-distribution or has outliers. Much of the framework for TBASS is adapted from the concepts of Bayesian Multivariate Adaptive Regression Splines (BMARS), specifically the work done by Denison, Mallick, and Smith (1998). The spline function is fit using a Reversible-Jump Markov Chain Monte Carlo algorithm,. By including this more robust generalization, a dataset with outliers can be accurately fit using the BMARS model, without the possibility of overfitting or variance inflation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Characterization of Partially Observed Epidemics - Application to COVID-19

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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

(U) Validation and Optimization of a 1-Dimensional Quasi-Isentropic Compression Model Using a Functionally Graded Material Flyer Plate

The fields studying highly compressed materials have been around for many years. These fields have given birth to better understandings of the solar fusion cycle and the liquid-metallic core of Jupiter. The highest pressures achieved have been at the National Ignition Facility (NIF) and more recently, the Z-Machine. One method to achieve higher levels of compression is to use quasi-isentropic (QI) compression. In practice, this means minimizing the amount of temperature increase of the target material. In their paper, J.H. Nguyen et. al. manufactured a functionally graded material (FGM) in order to help them achieve QI compression. The aim of the study described in this paper is to validate a 1-dimensional (1D) FLAG model that uses an FGM flyer to compress a copper (Cu) target. Once validated, the density/composition profile of the FGM is varied and optimized using Markov Chain Monte Carlo, specifically using a Metropolis-Hastings algorithm to find the optimal FGM profile that minimizes the temperature increase in the copper.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of CTF modeling of interfacial drag, wall shear, and interfacial heat transfer for bubbly and annular-mist flow regimes

This milestone report discusses improvements to CTF closure models for modeling of bubbly and annular mist two-phase flow conditions, which are important for accurate modeling and simulation for boiling water reactor (BWR) conditions. Models that were improved include the two-phase wall shear, wall boiling, and phase mass and heat transfer. A new flow regime map was implemented to improve on the prediction of transition to annular flow, which was generally largely over-predicted by the legacy CTF flow regime map. The annular mist interfacial drag models were also reviewed and new models were proposed for implementation into the code. In addition to implementing new models, an activity was performed to calibrate these new models to experimental data using a Markov Chain Monte Carlo technique, thus further improving their accuracy. Finally, new experiments were implemented into the CTF validation matrix which add more two-phase pressure drop and void measurements for the assessment of CTF for modeling of BWRs. The results show that the Chisholm model, which was implemented and then calibrated, results in significantly improved comparison against the Riso two-phase pressure drop data. Furthermore, improvements in the implicit heat transfer and wall boiling models resulted in good agreement with the void measurement data.

42 ENGINEERING↗
DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Trustworthy Physics-Informed Deep Learning for Predictive Scientific Computing

This project has developed powerful trustworthy physics-informed deep learning (TPiDL) models and methods to fundamentally enhance the scale and power of computational modeling in the scientific and engineering domains. Deep learning (DL) has radically advanced the state-of-the-art in machine learning, computer vision, natural language processing, and also scientific computing. Nevertheless, progress has been driven almost entirely by empirical observations, hacks, and tricks. Under the support of this project, the graph operator learning tools and advanced trustworthy physical informed neural networks have been developed. In addition, stochastic gradient replica-exchange Markov Chain Monte Carlo (MCMC) sampling algorithms have been designed to quantify the uncertainties and speed up the training of large-scale neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian Fit for the NOvA Three Flavor Oscillation Analysis

NOvA is a long baseline neutrino oscillation experiment, using Fermilab's NuMI beam and a functionally identical near and far detector. NOvA measures muon neutrino disappearance and electron neutrino appearance to probe neutrino oscillation parameters, including the large neutrino mixing angle, the mass ordering, and the CP-violating phase. NOvA has developed a Bayesian analysis in addition to its Frequentist analysis, using Markov Chain Monte Carlo. This Bayesian framework allows for measurements previously difficult to make with the Frequentist framework, such as the Jarlskog invariant and the reactor mixing angle. The details and status of the Bayesian Framework will be presented, as well as latest NOvA results on measurements of three-flavor oscillation parameters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Yttrium solid phase equation of state with uncertainty quantification

We discuss the development of an equation of state (EOS) for solid yttrium up to roughly 80 GPa. The EOS makes use of different experimental datasets, including measurements of the isobaric density, isobaric specific heat, room temperature isotherm, and principal shock Hugoniot. The fitting procedure is carried out using Markov Chain Monte Carlo (MCMC), where we fit model parameters for both the cold curve and ion thermal models simultaneously. The results show close agreement with experimental data and provide meaningful uncertainty estimates on the model parameters. This work serves as a first step towards a multiphase EOS for yttrium, which will include higher pressure solid phases (> 80 GPa), as well as modeling of the liquid phase.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Improving the Parameterization of Cloud and Rain Microphysics in E3SM using Novel Observationally-Constrained Bayesian Approach (Final Technical Report)

In this project, we sought to develop new cloud and rain microphysics frameworks within the Energy Exascale Earth System Model (E3SM). This work encompassed two primary avenues of research: 1) Further development of a Bayesian-based scheme called BOSS (Bayesian Observationally-constrained Statistical-physical Scheme) to represent cloud and rain microphysics, testing it in realistic high-resolution cloud models, and implementing it in E3SM; 2) Development of a methodology utilizing machine learning to enable computationally tractable use of tractable use of Markov chain Monte Carlo sampling for Bayesian parameter estimation in Earth system and cloud models. In this project, we adapted the BOSS microphysics scheme, originally formulated for rain-only, to include all liquid-phase microphysical processes for cloud and rain, in particular the processes that mediate between these two categories, for example the conversion from cloud to rain through collision and coalescence of drops. We constrained the scheme via comparison and testing against a detailed model that explicitly represents the evolution of cloud and rain particles, called a bin microphysics scheme.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scalable Algorithms for Inverse Problems With High-Dimensional Parameter Spaces

Inverse problems, which involve inferring unknown parameters from observed data, present significant computational challenges, especially in large-scale settings with high-dimensional unknown parameters and nonlinear relationships between the unknowns and observations. Bayesian inference provides an approach for addressing these problems, often relying on sequential sampling methods like Markov chain Monte Carlo (MCMC) to approximate the posterior distribution of the parameters. However, MCMC methods become computationally demanding as the dimensionality of the problem increases, particularly in large-scale systems where likelihood evaluations rely on solving partial differential equations (PDEs) on large spatial domains with finely resolved meshes. To overcome these limitations, recent advancements have focused on designing scalable computa tional techniques – for both PDE simulations and sampling strategies – to make Bayesian methods feasible for high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PRIME - A Software Toolkit for the Characterization of Partially Observed Epidemics in a Bayesian Framework

PRIME is a modeling framework designed for the “real-time’” characterization and forecasting of partially observed epidemics. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov Chain Monte Carlo technique. The framework can accommodate multiple epidemic waves and can help identify different disease dynamics at the regional, state, and country levels. We include examples using publicly available COVID-19 data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Probing Non-Standard Interactions in NOvA with Bayesian MCMC Methods

We present a search for non-standard neutrino interactions (NSI) using NOvA's joint $\nu_e$ appearance and $\nu_\mu$ disappearance samples in both neutrino and antineutrino beam modes. A Bayesian Markov Chain Monte Carlo approach is used to map the posterior distribution over NSI parameters $\varepsilon_{e\mu}$, $\varepsilon_{e\tau}$, and $\varepsilon_{\mu\tau}$, simultaneously with standard oscillation parameters. We investigate the effect of prior choice for the complex NSI parameters and present projected sensitivity to off-diagonal NSI.

Huang, Xiaoyan [Mississippi U.]↗
DOE OSTI2021

Evaluating the Benefits of Bayesian Hierarchical Methods for Analyzing Heterogeneous Environmental Datasets: A Case Study of Marine Organic Carbon Fluxes

Large compilations of heterogeneous environmental observations are increasingly available as public databases, allowing researchers to test hypotheses across datasets. Statistical complexities arise when analyzing compiled data due to unbalanced spatial sampling, variable environmental context, mixed measurement techniques, and other reasons. Hierarchical Bayesian modeling is increasingly used in environmental science to describe these complexities, however few studies explicitly compare the utility of hierarchical Bayesian models to simpler and more commonly applied methods. Here we demonstrate the utility of the hierarchical Bayesian approach with application to a large compiled environmental dataset consisting of 5,741 marine vertical organic carbon flux observations from 407 sampling locations spanning eight biomes across the global ocean. We fit a global scale Bayesian hierarchical model that describes the vertical profile of organic carbon flux with depth. Profile parameters within a particular biome are assumed to share a common deviation from the global mean profile. Individual station-level parameters are then modeled as deviations from the common biome-level profile. The hierarchical approach is shown to have several benefits over simpler and more common data aggregation methods. First, the hierarchical approach avoids statistical complexities introduced due to unbalanced sampling and allows for flexible incorporation of spatial heterogeneitites in model parameters. Second, the hierarchical approach uses the whole dataset simultaneously to fit the model parameters which shares information across datasets and reduces the uncertainty up to 95% in individual profiles. Third, the Bayesian approach incorporates prior scientific information about model parameters; for example, the non-negativity of chemical concentrations or mass-balance, which we apply here. We explicitly quantify each of these properties in turn. We emphasize the generality of the hierarchical Bayesian approach for diverse environmental applications and its increasing feasibility for large datasets due to recent developments in Markov Chain Monte Carlo algorithms and easy-to-use high-level software implementations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer

Restricted Boltzmann Machine (RBM) is an energy-based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate the exact gradient of the log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), where obtaining samples is faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results of RBM trained using quantum annealing are compared with the CD-based method. The performance of the two approaches is compared with respect to the classification accuracies, image reconstruction, and log-likelihood results. The classification accuracy results indicate comparable performances of the two methods. Image reconstruction and log-likelihood results show improved performance of the CD-based method. It is shown that the samples obtained from quantum annealer can be used to train an RBM on a 64-bit “bars and stripes” dataset with classification performance similar to an RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer could be useful as it eliminates computationally expensive MCMC steps of CD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Performing Bayesian Analyses With AZURE2 Using BRICK: An Application to the 7 Be System

Phenomenological R-matrix has been a standard framework for the evaluation of resolved resonance cross section data in nuclear physics for many years. It is a powerful method for comparing different types of experimental nuclear data and combining the results of many different experimental measurements in order to gain a better estimation of the true underlying cross sections. Yet a practical challenge has always been the estimation of the uncertainty on both the cross sections at the energies of interest and the fit parameters, which can take the form of standard level parameters. Frequentist (χ 2 -based) estimation has been the norm. In this work, a Markov Chain Monte Carlo sampler, emcee, has been implemented for the R-matrix code AZURE2, creating the Bayesian R-matrix Inference Code Kit (BRICK). Bayesian uncertainty estimation has then been carried out for a simultaneous R-matrix fit of the 3 He (α,γ) 7 Be and 3 He (α,α) 3 He reactions in order to gain further insight into the fitting of capture and scattering data. Both data sets constrain the values of the bound state α-particle asymptotic normalization coefficients in 7 Be. The analysis highlights the need for low-energy scattering data with well-documented uncertainty information and shows how misleading results can be obtained in its absence.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Analyzing rotational bands in odd-mass nuclei using effective field theory and Bayesian methods

We recently developed an Effective Field Theory (EFT) for rotational bands in odd-mass nuclei. Here we use EFT expressions to perform a Bayesian analysis of data on the rotational energy levels of 99 Tc, 155,157 Gd, 159 Dy, 167,169 Er, 167,169 Tm, 183 W, 235 U and 239 Pu. The error model in our Bayesian analysis includes both experimental and EFT truncation uncertainties. It also accounts for the fact that low-energy constants (LECs) at even and odd orders are expected to have different sizes. We use Markov Chain Monte Carlo (MCMC) sampling to explore the joint posterior of the EFT and error-model parameters and show both the LECs and the breakdown scale can be reliably determined. We extract the LECs up to fourth order in the EFT and find that, provided we correctly account for EFT truncation errors in our likelihood, results for lower-order LECs are stable as we go to higher orders. LEC results are also stable with respect to the addition of higher-energy data. We extract the expansion parameter for all the nuclei listed above and find a clear correlation between the extracted and the expected value of the inverse breakdown scale, W, based on the single-particle and vibrational energy scales. However, the W that actually determines the convergence of the EFT expansion is markedly smaller than would be naively expected based on those scales.

EFT↗