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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗
DOE OSTI2018

Probabilistic inference of reaction rate parameters from summary statistics

Here, this investigation tackles the probabilistic parameter estimation problem involving the Arrhenius parameters for the rate coefficient of the chain branching reaction H + O 2 → OH + O. This is achieved in a Bayesian inference framework that uses indirect data from the literature in the form of summary statistics by approximating the maximum entropy solution with the aid of approximate bayesian computation. The summary statistics include nominal values and uncertainty factors of the rate coefficient, obtained from shock-tube experiments performed at various initial temperatures. The Bayesian framework allows for the incorporation of uncertainty in the rate coefficient of a secondary reaction, namely OH + H 2 → H 2 O + H, resulting in a consistent joint probability density on Arrhenius parameters for the two rate coefficients. It also allows for uncertainty quantification in numerical ignition predictions while conforming with the published summary statistics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Probabilistic inference of basic stellar parameters: application to flickering stars

The relations between observable stellar parameters are usually assumed to be deterministic. That is, given an infinitely precise measurement of independent variable, “x”, and some model, the value of dependent variable, “y” can be known exactly. In practice this assumption is rarely valid and intrinsic stochasticity means that two stars with exactly the same “x” will have slightly different “y”s. The relation between short-timescale brightness fluctuations (flicker) of stars and both surface gravity and stellar density are two such stochastic relations that have until now been treated as deterministic ones. We recalibrate these relations in a probabilistic framework, using hierarchical Bayesian modeling to constrain the instrinsic scatter in the relations. We find evidence for additional scatter in the relationship, that cannot be accounted for by the observational uncertainties alone. The scatter in surface gravity and stellar density does not depend on flicker, suggesting that using flicker as a proxy for logg and ρ {sub ⋆} is equally valid for dwarf and giant stars, despite the fact that the observational uncertainties tend to be larger for dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Probabilistic inference of the structure and orbit of Milky Way satellites with semi-analytic modelling

Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way’s classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity v max and the radius r max at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for v max and r max , this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite’s dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. Furthermore, the technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Probabilistic inference in very large universes

Our current favored cosmological theories allow for the striking and controversial possibility that the observable universe is just a small part of a much larger universe in which parameters that describe the effective, low-energy laws of physics vary from one region to another. The controversy is largely driven by the fact that such a “very large universe” is mostly observationally inaccessible to us, so the issue arises of how we can reasonably assess a theory that describes such a universe. In this paper, we propose a Bayesian method for theory assessment based on theory-generated probability distributions for our observations. We focus on the principles that define this method, leaving aside concerns about how, in practice, one would carry out the required calculations. (One important issue that we set aside is the measure problem.) We argue that cosmological theories can be tested by the standard method of Bayesian updating, but we need to use theoretical predictions for “first-person” probabilities—that is, probabilities that we should use for our observations, taking into account all relevant selection effects. These selection effects can vary from one observer to another and can vary with time, so, in principle, first-person probabilities are defined for each observer instant—an observer at a specific instant of time. Calculations of first-person probabilities should take into account everything that the observer believes about herself and her surroundings, which we refer to as her subjective state. If the universe is very large, a theory might predict that there are many observer instants in the same subjective state; we argue that first-person probabilities should be calculated using a principle of self-locating indifference (PSLI), the assumption that any real observer should make predictions for her future as if she were chosen randomly and uniformly from the theoretically predicted observer instants that share her subjective state. We believe the PSLI is intuitively very reasonable, but we also argue that, if the theory is correct, the use of this principle maximizes the expected fraction of observers who will make correct predictions. A further complication is that cosmological theories are not expected to fully predict the detailed properties of the universe, but rather will predict a set of possible universes, each with a probability. Different possible universes will generically have different numbers of observers. We argue that, in the calculation of first-person probabilities, the probability for each possible universe should be weighted by the number of observer instants in the specified subjective state that it contains. These issues have been controversial in the literature, so we also provide a rebuttal to the claim that principles like the PSLI involve a “selection fallacy”; a rebuttal to what we dub the principle of required certainty; an argument rejecting theories that predict a preponderance of Boltzmann brains; a rebuttal to a parable about humans and Jovians used by Hartle and Srednicki to argue that assumptions of typicality can lead to absurd consequences; and, finally, a discussion about how the use of “old evidence” can be fit into a Bayesian mold.

Azhar, Feraz [University of Notre Dame, IN (United↗
DOE OSTI2026

Probabilistic Inference of Low-Surface-Brightness Galaxy Morphological Parameters Using Simulation-Based Inference

Low-surface-brightness galaxies (LSBGs) are diffuse, often dark-matter-dominated systems whose faintness makes their structural parameters difficult to measure reliably in wide-field imaging surveys. Robust parameter inference, including uncertainty quantification, is important for population studies and for comparisons with models of galaxy formation, as future surveys are expected to produce increasingly large samples of diffuse galaxies. In practice, LSBG profile modeling is sensitive to sky- background errors, masking choices, contaminating background sources, and the computational cost of obtaining posterior-level uncertainties for large samples. Motivated by these questions, we develop a simulation-based inference (SBI) framework for estimating posterior distributions of LSBG morphological parameters from simulated galaxy images. Using PyImfit, we generate DES-like single-Sersic profile LSBG images with known position angle, ellipticity, Sersic index, effective surface brightness, and effective radius. We then train a normalizing-flow-based neural posterior estimator using the sbi package to infer these parameters from the simulated images. For isolated simulated galaxies, the SBI posterior recovers the true input parameters, produces posterior predictive residuals consistent with the assumed noise model, and shows good empirical calibration in a DES-motivated test regime. We also compare SBI with PyImfit-based MCMC inference and find broadly comparable posterior constraints, while SBI enables substantially faster posterior sampling after training. Finally, we test robustness to compact background contaminants. A model trained only on isolated galaxies produces undercovered posteriors on contaminated images, whereas training on simulations with variable contaminant positions and fluxes improves calibration across contaminated test sets. These results demonstrate the promise of SBI for scalable, uncertainty-aware LSBG morphology inference, while emphasizing that posterior reliability strongly depends on whether training simulations include relevant observational complications.

Batbayar, Bilguun [U. Chicago (main)]↗
DOE OSTI2022

Human reliability analysis studies from simulator experiments using Bayesian inference

Probabilistic Safety Assessment (PSA) of complex facilities is performed to arrive at the risk posed by them. PSA also accounts for the contribution of the human errors towards the overall risk through Human Reliability Analysis (HRA) in terms of Human Error Probability (HEP). Human operators are part of the system and do not work in isolation. Their performance is influenced by the context in which the actions are performed. As a result, quantification of HEP requires operator performance data under the given context. Some good sources of operator performance data are plant‘s operation data, simulator data and expert judgement. The plant operation data pertaining to HRA is generally sparse. In this situation, a full scope plant simulator provides a good alternative for operator performance data generation. Many of the currently practiced HRA methods have been developed by combining the empirical evidence with expert judgement and contain a lot of uncertainty in their estimates. Bayesian inference is suitable for updating the prior HRA estimates with the simulator evidence to obtain the posterior HEP. Here, posterior HEP has been calculated for postulated accident scenarios in advanced reactor (first of its kind) at design stage, using plant simulator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimization, Inference and Learning for Advanced Networks

This piece of software aims at providing an implementation of methods to perform efficient optimization, probabilistic inference and model selection and learning for various type of networked systems. The software contains a suite of methods and functions that: 1) perform probabilistic inference and random sampling on networked models 2) select or learn networked models from data 3) solve optimization tasks with a networked structure Network models that are supported by these features include, but are not limited to, factor graphs and Markov random fields such as Ising models, Potts models, p-spins models, Gaussian random fields and restricted Boltzmann machine.

Hijazi, Hassan↗
NASA NTRS1992

Experiments with a decision-theoretic scheduler

This paper describes DTS, a decision-theoretic scheduler designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems, and using probabilistic inference to aggregate this information in light of features of a given problem. BPS, the Bayesian Problem-Solver, introduced a similar approach to solving single-agent and adversarial graph search problems, yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Hansson, Othar↗
NASA NTRS1993

Decision-theoretic control of EUVE telescope scheduling

This paper describes a decision theoretic scheduler (DTS) designed to employ state-of-the-art probabilistic inference technology to speed the search for efficient solutions to constraint-satisfaction problems. Our approach involves assessing the performance of heuristic control strategies that are normally hard-coded into scheduling systems and using probabilistic inference to aggregate this information in light of the features of a given problem. The Bayesian Problem-Solver (BPS) introduced a similar approach to solving single agent and adversarial graph search patterns yielding orders-of-magnitude improvement over traditional techniques. Initial efforts suggest that similar improvements will be realizable when applied to typical constraint-satisfaction scheduling problems.

Hansson, Othar↗
DOE OSTI2022

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2011

Verification of Small Hole Theory for Application to Wire Chaffing Resulting in Shield Faults

Our work is focused upon developing methods for wire chafe fault detection through the use of reflectometry to assess shield integrity. When shielded electrical aircraft wiring first begins to chafe typically the resulting evidence is small hole(s) in the shielding. We are focused upon developing algorithms and the signal processing necessary to first detect these small holes prior to incurring damage to the inner conductors. Our approach has been to develop a first principles physics model combined with probabilistic inference, and to verify this model with laboratory experiments as well as through simulation. Previously we have presented the electromagnetic small-hole theory and how it might be applied to coaxial cable. In this presentation, we present our efforts to verify this theoretical approach with high-fidelity electromagnetic simulations (COMSOL). Laboratory observations are used to parameterize the computationally efficient theoretical model with probabilistic inference resulting in quantification of hole size and location. Our efforts in characterizing faults in coaxial cable are subsequently leading to fault detection in shielded twisted pair as well as analysis of intermittent faulty connectors using similar techniques.

Schuet, Stefan R.↗
DOE OSTI2026

py-boomer v0.1.0

Py-BOOMER (Python Bayesian OWL Ontology MErgER in Python) is a probabilistic reasoning system for knowledge representation and ontological reasoning with uncertainty. Itnables reasoning over probabilistic facts and taxonomic relationships, finding the most likely consistent interpretation of potentially conflicting assertions. It uses a combination of graph-based reasoning and Bayesian probabilistic inference. Key features: Represent probabilistic ontological statements Reason over class subsumption hierarchies Evaluate class equivalence relationships Detect and resolve logical inconsistencies Calculate posterior probabilities for each assertion

Mungall, Chris [Lawrence Berkeley National Laborat↗
NASA NTRS1995

Real-time value-driven diagnosis

Diagnosis is often thought of as an isolated task in theoretical reasoning (reasoning with the goal of updating our beliefs about the world). We present a decision-theoretic interpretation of diagnosis as a task in practical reasoning (reasoning with the goal of acting in the world), and sketch components of our approach to this task. These components include an abstract problem description, a decision-theoretic model of the basic task, a set of inference methods suitable for evaluating the decision representation in real-time, and a control architecture to provide the needed continuing coordination between the agent and its environment. A principal contribution of this work is the representation and inference methods we have developed, which extend previously available probabilistic inference methods and narrow, somewhat, the gap between probabilistic and logical models of diagnosis.

Dambrosio, Bruce↗
NASA NTRS2009

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

Dezfuli, Homayoon↗
NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗