Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “BAYESIAN STATISTICS”

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

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

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Characterizing the Uncertainty of Measurement of Traceable Isotope Ratios with Bayesian Statistical Techniques

Analytical techniques such as multicollector—inductively coupled plasma—mass spectrometry (MC-ICP-MS) are routinely employed at SRNL, other National Laboratories, and in academia to determine the precise isotopic composition of diverse natural and anthropogenic samples (e.g., rocks and nuclear materials). Quantifying and reporting uncertainty in such analyses, while regularly performed, have a rigorous statistical foundation. The Guide to the Expression of Uncertainty in Measurement 4 (GUM) outlines conventional techniques used to assess such uncertainty. As the accessibility and speed of statistical computing increase, there is a need to modernize conventional techniques. For example, Supplement 1 to the 3rd to the GUM suggests the use of approximation methods as an updated approach to the GUM.

McLarty, Ellis C.↗
DOE OSTI2025

Bayesian Statistical Analysis for Mass Spectrometric Data Processing

• Thermal ionization mass spectrometry (TIMS) is a “gold standard’ technique for actinide isotope amount ratio and assay measurements. • Ubiquitously used in: • Nuclear Nonproliferation • Nuclear Safeguards • Nuclear Forensics • Basic Science • Savannah River National Laboratory (SRNL) installed a new Thermo Scientific TRITON Plus TIMS in early 2024.

McLarty, Ellis C. [Savannah River National Laborat↗
DOE OSTI2025

Bayesian Statistical Analysis for Mass Spectrometric Data Processing

Thermal ionization mass spectrometry (TIMS) is a “gold standard’ technique for actinide isotope amount ratio and assay measurements. • Ubiquitously used in: • Nuclear Nonproliferation • Nuclear Safeguards • Nuclear Forensics • Basic Science • Savannah River National Laboratory (SRNL) installed a new Thermo Scientific TRITON Plus TIMS in early 2024.

McLarty, Ellis C. [Savannah River National Laborat↗
NASA NTRSDate not supplied

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗
DOE OSTI2016

BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Developing a Comprehensive Risk Assessment Framework for Geological Storage CO 2

The operational risks for CCS projects include: risks of capturing, compressing, transporting and injecting CO₂; risks of well blowouts; risk that CO 2 will leak into shallow aquifers and contaminate potable water; and risk that sequestered CO 2 will leak into the atmosphere. This report examines these risks by using information on the risks associated with analogue activities such as CO 2 based enhanced oil recovery (CO 2 -EOR), natural gas storage and acid gas disposal. We have developed a new analysis of pipeline risk based on Bayesian statistical analysis. Bayesian theory probabilities may describe states of partial knowledge, even perhaps those related to non-repeatable events. The Bayesian approach enables both utilizing existing data and at the same time having the capability to adsorb new information thus to lower uncertainty in our understanding of complex systems. Incident rates for both natural gas and CO 2 pipelines have been widely used in papers and reports on risk of CO 2 pipelines as proxies for the individual risk created by such pipelines. Published risk studies of CO 2 pipelines suggest that the individual risk associated with CO2 pipelines is between 10 -3 and 10 -4 , which reflects risk levels approaching those of mountain climbing, which many would find unacceptably high. This report concludes, based on a careful analysis of natural gas pipeline failures, suggests that the individual risk of CO 2 pipelines is likely in the range of 10-6 to 10-7, a risk range considered in the acceptable to negligible range in most countries. If, as is commonly thought, pipelines represent the highest risk component of CCS outside of the capture plant, then this conclusion suggests that most (if not all) previous quantitative- risk assessments of components of CCS may be orders of magnitude to high. The potential lethality of unexpected CO 2 releases from pipelines or wells are arguably the highest risk aspects of CO 2 enhanced oil recovery (CO2-EOR), carbon capture, and storage (CCS). Assertions in the CCS literature, that CO 2 levels of 10% for ten minutes, or 20 to 30% for a few minutes are lethal to humans, are not supported by the available evidence. The results of published experiments with animals exposed to CO 2 , from mice to monkeys, at both normal and depleted oxygen levels, suggest that lethal levels of CO 2 toxicity are in the range 50 to 60%. These experiments demonstrate that CO 2 does not kill by asphyxia, but rather is toxic at high concentrations. It is concluded that quantitative risk assessments of CCS have overestimated the risk of fatalities by using values of lethality a factor two to six lower than the values estimated in this paper. In many dispersion models of CO 2 releases from pipelines, no fatalities would be predicted if appropriate levels of lethality for CO 2 had been used in the analysis.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

Exploring the Connection Between Sampling Problems in Bayesian Inference and Statistical Mechanics

The Bayesian and statistical mechanical communities often share the same objective in their work - estimating and integrating probability distribution functions (pdfs) describing stochastic systems, models or processes. Frequently, these pdfs are complex functions of random variables exhibiting multiple, well separated local minima. Conventional strategies for sampling such pdfs are inefficient, sometimes leading to an apparent non-ergodic behavior. Several recently developed techniques for handling this problem have been successfully applied in statistical mechanics. In the multicanonical and Wang-Landau Monte Carlo (MC) methods, the correct pdfs are recovered from uniform sampling of the parameter space by iteratively establishing proper weighting factors connecting these distributions. Trivial generalizations allow for sampling from any chosen pdf. The closely related transition matrix method relies on estimating transition probabilities between different states. All these methods proved to generate estimates of pdfs with high statistical accuracy. In another MC technique, parallel tempering, several random walks, each corresponding to a different value of a parameter (e.g. "temperature"), are generated and occasionally exchanged using the Metropolis criterion. This method can be considered as a statistically correct version of simulated annealing. An alternative approach is to represent the set of independent variables as a Hamiltonian system. Considerab!e progress has been made in understanding how to ensure that the system obeys the equipartition theorem or, equivalently, that coupling between the variables is correctly described. Then a host of techniques developed for dynamical systems can be used. Among them, probably the most powerful is the Adaptive Biasing Force method, in which thermodynamic integration and biased sampling are combined to yield very efficient estimates of pdfs. The third class of methods deals with transitions between states described by rate constants. These problems are isomorphic with chemical kinetics problems. Recently, several efficient techniques for this purpose have been developed based on the approach originally proposed by Gillespie. Although the utility of the techniques mentioned above for Bayesian problems has not been determined, further research along these lines is warranted

Pohorille, Andrew↗
DOE OSTI2021

Estimating uncertainty: A Bayesian approach to modelling photosynthesis in C3 leaves

The Farquhar-von Caemmerer-Berry (FvCB) model is extensively used to model pho-tosynthesis from gas exchange measurements. Since its publication, many methods have been developed to measure, or more accurately estimate, parameters of this model. Here, we have created a tool that uses Bayesian statistics to fit photosyn-thetic parameters using concurrent gas exchange and chlorophyll fluorescence mea-surements whilst evaluating the reliability of the parameter estimation. We have tested this tool on synthetic data and experimental data from rice leaves. Our results indicate that reliable parameter estimation can be achieved whilst only keeping one parameter, Km, that is, Michaelis constant for CO2 by Rubisco, prefixed. Additionally, we show that including detailed low CO2 measurements at low light levels increases reliability and suggests this as a new standard measurement protocol. By providing an estimated distribution of parameter values, the tool can be used to evaluate the quality of data from gas exchange and chlorophyll fluorescence measurement proto-cols. Compared to earlier model fitting methods, the use of a Bayesian statistics-based tool minimizes human interaction during fitting, reducing the subjectivity which is essential to most existing tools. A user friendly, interactive Bayesian tool script is provided.

Bayesian statistics, leaf photosynthesis, mesophyl↗
DOE OSTI2015

Adaptive Bayesian Inference for Prediction

Performing rigorous predictive simulation necessitates that all available data be incorporated into the modeling process in order to accurately quantify uncertainties in predictions of the model. Statistical Bayesian inference is the most commonly used approach for incorporating data into uncertainty estimates. Statistical Bayesian inference uses data and an assumed error model to inform posterior distributions of model input variables and model discrepancies. An explicit characterization of the posterior distribution is not formed, but rather methods such as Markov chain Monte-Carlo (MCMC) are used to draw samples from the posterior. These samples are then used to evaluate statistics, such as integrals, of predicted quantities of interest (QoI). Recently the authors have developed an alternative reformulation of Bayesian inference based upon a measure-theoretic inversion technique. Unlike traditional statistical Bayesian inference, this reformulation allows us to directly interrogate the posterior distribution and, consequently, to generate samples from the posterior without MCMC. In this work we use forward propagation uncertainty quantification techniques to reduce the cost of approximating the posterior distribution. Specifically we investigate goal-oriented adaptive sparse grid approximation methods. These adaptive methods concentrate high-fidelity model evaluations in regions of the parameter space that both significantly contribute to the uncertainty in specified QoI and are informed by the available data. Numerical results are presented to compare our approach with the standard statistical Bayesian approach and to demonstrate the effectiveness of the goal-oriented adaptivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗