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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Forecasting Multi-Wave Epidemics Through Bayesian Inference

We present a simple, near-real-time Bayesian method to infer and forecast a multiwave outbreak, and demonstrate it on the COVID-19 pandemic. The approach uses timely epidemiological data that has been widely available for COVID-19. It provides short-term forecasts of the outbreak’s evolution, which can then be used for medical resource planning. The method postulates one- and multiwave infection models, which are convolved with the incubation-period distribution to yield competing disease models. The disease models’ parameters are estimated via Markov chain Monte Carlo sampling and information-theoretic criteria are used to select between them for use in forecasting. The method is demonstrated on two- and three-wave COVID-19 outbreaks in California, New Mexico and Florida, as observed during Summer-Winter 2020. We find that the method is robust to noise, provides useful forecasts (along with uncertainty bounds) and that it reliably detected when the initial single-wave COVID-19 outbreaks transformed into successive surges as containment efforts in these states failed by the end of Spring 2020.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Discovering causal structure with reproducing-kernel Hilbert space ε -machines

We merge computational mechanics’ definition of causal states (predictively equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely applicable method that infers causal structure directly from observations of a system’s behaviors whether they are over discrete or continuous events or time. A structural representation—a finite- or infinite-state kernel ϵ-machine—is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker–Planck equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high-dimensional data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1976

Aeronomy report no. 74: The Urbana meteor-radar system; design, development, and first observations

The design, development, and first observations of a high power meteor-radar system located near Urbana, Illinois are described. The roughly five-fold increase in usable echo rate compared to other facilities, along with automated digital data processing and interferometry measurement of echo arrival angles, permits unsurpassed observations of tidal structure and shorter period waves. Such observations are discussed. The technique of using echo decay rates to infer density and scale height and the method of inferring wind shear from radial acceleration are examined. An original experiment to test a theory of the Delta-region winter anomaly is presented.

Hess, G. C.↗
NASA NTRS2012

Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

The main point of this study was to use realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE satellite mission, was applied to CloudSat, CALIPSO, and MODIS satellite data thus producing 3D cloudy atmospheres measuring 60 km wide by 13,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances for each (1 km)2 column where then produced by a Monte Carlo photon transfer model run in both full 3D and independent column approximation mode (i.e., a 1D model).

Barker, Howard W.↗
DOE OSTI2024

Approach for Inferring Full-Scope Human Reliability Data Based on Simplified Simulator Data

This paper proposes a method for inferring full-scope human reliability data based on the Simplified Human Error Experimental Program (SHEEP) data. It mainly focuses on the human errors observed when using simulators with different complexity levels. In the proposed method, the manner in which human error probabilities (HEPs) change as a result of increasing simulator complexity and how simulator complexity levels are quantified represent key information for inferring full-scope data. In the present study, SHEEP error data pertaining to actual professional operators using Rancor Microworld (Rancor) (i.e., a more simplified simulator) and Compact Nuclear Simulator (CNS) (i.e., a less simplified simulator) were compared with the HuREX error data. An approach to quantifying simulator complexity levels was then proposed based on information theory and acquired eye-tracker data.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1989

Inference of stratospheric aerosol composition and size distribution from SAGE II satellite measurements

A method for inferring stratospheric aerosol composition and size distribution from the water vapor concentration and aerosol extinction measurements obtained in the Stratospheric Aerosol and Gas Experiment (SAGE) II and the associated temperature from the NMC. The aerosols are assumed to be sulfuric acid-water droplets. A modified Levenberg-Marquardt algorithm is used to determine model size distribution parameters based on the SAGE II multiwavelength aerosol extinctions. It is found that the best aerosol size information is contained in the aerosol radius range between about 0.25 and 0.80 micron.

Wang, Pi-Huan↗
NASA NTRS1972

The inference of atmospheric ozone using satellite horizon measurements in the 1042 per cm band.

Description of a method for inferring atmospheric ozone information using infrared horizon radiance measurements in the 1042 per cm band. An analysis based on this method proves the feasibility of the horizon experiment for determining ozone information and shows that the ozone partial pressure can be determined in the altitude range from 50 down to 25 km. A comprehensive error study is conducted which considers effects of individual errors as well as the effect of all error sources acting simultaneously. The results show that in the absence of a temperature profile bias error, it should be possible to determine the ozone partial pressure to within an rms value of 15 to 20%. It may be possible to reduce this rms error to 5% by smoothing the solution profile. These results would be seriously degraded by an atmospheric temperature bias error of only 3 K; thus, great care should be taken to minimize this source of error in an experiment. It is probable, in view of recent technological developments, that these errors will be much smaller in future flight experiments and the altitude range will widen to include from about 60 km down to the tropopause region.

Russell, J. M., III↗
DOE OSTI2023

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Retrieval of aerosol size distribution moments from multiwavelength particulate extinction measurements

Two methods for inferring aerosol size distribution moments from multiwavelength particulate extinction measurements are studied. The methods are an eigenvalue technique that approximates an appropriate moment-weighting function by a linear combination of kernel functions and a conversion ratio approach that uses the ratio of the particulate extinction measurements at two wavelengths to choose a model moment-to-extinction conversion ratio. The techniques are applied to infer the third moment, or volume, of the aerosol size distribution from actual particulate extinction measurements taken as part of the Stratospheric Aerosol and Gas Experiment II during a correlative measurement experiment in Brazil in April 1985.

Livingston, John M.↗
DOE OSTI2019

Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Single cells exhibit a significant amount of variability in transcript levels, which arises from slow, stochastic transitions between gene expression states. Elucidating the nature of these states and understanding how transition rates are affected by different regulatory mechanisms require state-of-the-art methods to infer underlying models of gene expression from single cell data. A Bayesian approach to statistical inference is the most suitable method for model selection and uncertainty quantification of kinetic parameters using small data sets. However, this approach is impractical because current algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piecewise deterministic Markov process. Here, we combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We demonstrate that kinetic parameters can be reasonably constrained for modestly sampled data sets if the model is known a priori. If there are many competing models, Bayesian evidence can rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics typically used for model selection.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

On Bayesian Inductive Inference & Predictive Estimation

We investigate Bayesian inference and the Principle of Maximum Entropy (PME) as methods for doing inference under uncertainty. This investigation is primarily through concrete examples that have been previously investigated in the literature. We find that it is possible to do Bayesian inference and PME inference using the same information, despite claims to the contrary, but that the results are not directly comparable. This is because Bayesian inference yields a probability density function (pdf) over the unknown model parameters, whereas PME yields point estimates. If mean estimates are extracted from the Bayesian pdfs, the resulting parameter estimates can differ radically from the PME values and also from the Maximum Likelihood values. We conclude that these differences are due to the Bayesian inference not assuming anything beyond the given prior probabilities and the data, whereas PME implicitly assumes that the given constraints are the only constraints that are operating. Since this assumption can be wrong, PME values may have to be revised when subsequent data shows evidence for more constraints. The entropy concentration previously "proved" by E. T. Jaynes is shown to be in error. Further, we show that PME is a generalized form of independence assumption, and so can be a very powerful method of inference when the variables being investigated are largely independent of each other.

Cheeseman, Peter↗
NASA NTRS2017

On Tidal Inference in the Diurnal Band

Standard methods of tidal inference should be revised to account for a known resonance that occurs mostly within the K(sub 1) tidal group in the diurnal band. The resonance arises from a free rotational mode of Earth caused by the fluid core. In a set of 110 bottom-pressure tide stations, the amplitude of the P(sub 1) tidal constituent is shown to be suppressed relative to K(sub 1), which is in good agreement with the resonance theory. Standard formulas for the K(sub 1) nodal modulation remain essentially unaffected. Two examples are given of applications of the refined inference methodology: one with monthly tide gauge data and one with satellite altimetry. For some altimeter-constrained tide models, an inferred P(sub 1) constituent is found to be more accurate than a directly determined one.

Ray, R. D.↗
DOE OSTI2025

EMPDF : inferring the Milky Way mass with data-driven distribution function in phase space

We introduce the emPDF (empirical distribution function), a novel dynamical modelling method that infers the gravitational potential from kinematic tracers with optimal statistical efficiency under the minimal assumption of steady state. emPDF determines the best-fitting potential by maximizing the similarity between instantaneous kinematics and the time-averaged phase-space distribution function (DF), which is empirically constructed from observation upon the theoretical foundation of oPDF (Han et al. 2016). This approach eliminates the need for presumed functional forms of DFs or orbit libraries required by conventional DF- or orbit-based methods. emPDF stands out for its flexibility, efficiency, and capability in handling observational effects, making it preferable to the popular Jeans equation or other minimal assumption methods, especially for the Milky Way (MW) outer halo where tracers often have limited sample size and poor data quality. We apply emPDF to infer the MW mass profile using Gaia DR3 data of satellite galaxies and globular clusters, obtaining enclosed masses of M (,r) = 26±8, 46±8, 90±13⁠, and 149±40 x 10 10 M ⊙ at r = 30, 50, 100⁠, and 200 kpc, respectively. These are consistent with the updated constraints from simulation-informed DF fitting (Li et al. 2020). While the simulation-informed DF offers superior precision owing to the additional information extracted from simulations, emPDF is independent of such supplementary knowledge and applicable to general tracer populations. emPDF is currently implemented for tracers with complete 6D kinematics within spherical potentials, but it can potentially be extended to address more general problems.

Astrophysics of Galaxies (astro-ph.GA)↗
DOE OSTI2025

Measurements of the polarization of several instabilities in the DIII-D tokamak

Recently, a method to infer the polarization of modes with frequencies much less than the ion cyclotron frequency was published [X.D. Du et al., Phys. Rev. Lett. 132 (2024) 215101]. The method uses measurements of electron temperature and density fluctuations δTₑ and δnₑ at the same spatial position to infer the local ratio of “acoustic polarization,” |δϕ ∥ |/(|δϕ ∥ |+|δψ|), where δϕ ∥ is the effective parallel potential and δψ is related to the parallel magnetic vector potential A ∥ . This paper summarizes key formulas, with emphasis on their range of validity, and elaborates on the workflow required to infer the acoustic polarization from experimental data. The drift-acoustic polarization of ellipticity-induced, toroidicity-induced, and reversed shear Alfvén eigenmodes is nearly zero, as expected for modes with predominately shear-Alfvénic polarization. The polarization of beta-induced Alfvén eigenmodes contains an acoustic component that increases with poloidal wave number. “Low frequency modes,” (instabilities that appear transiently when the minimum of the safety factor qₘᵢₙ passes through rational values) have large and highly variable acoustic polarization. In both experiment and simulation, fishbones have non-zero acoustic polarization that increases as the mode chirps down in frequency.

Alfven eigenmode↗
DOE OSTI2021

Automating Bayesian inference and design to quantify acoustic particle levitation

Self-propulsion of micro- and nanoparticles powered by ultrasound provides an attractive strategy for the remote manipulation of colloidal matter using biocompatible energy inputs. Quantitative understanding of particle motion and its dependence on size, shape, and composition requires accurate characterization of the acoustic field, which depends sensitively on the experimental setup. Here, we show how automated experiments based on Bayesian inference and design can accurately and efficiently characterize the acoustic field within resonant chambers used to propel acoustic nanomotors. Repeated cycles of observation, inference, and design (OID) are guided by a physical model that describes the rate at which levitating particles approach the nodal plane. Using video microscopy, we observe the relaxation of tracer particles to this plane following the application of the acoustic field. We use sequential Monte Carlo methods to infer model parameters such as the amplitude and frequency of the resonant chamber while accounting for particle-level measurement noise and population-level heterogeneity in the field. Guided by simulated outcomes, we select the optimal design for the next experiment as to maximize the information gain in the relevant parameters. We show how this iterative process serves to discriminate between competing hypotheses and efficiently converges to accurate parameter estimates using only few automated experiments. We discuss the need for model criticism to ensure the validity of the guiding model throughout automated cycles of observation, inference, and design. Furthermore, this work demonstrates how Bayesian methods can learn the parameters of nonlinear, hierarchical models used to describe video microscopy data of active colloids.

42 ENGINEERING↗
DOE OSTI2022

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Here, we propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple “snapshots.” Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates “fake” sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks. We refer to this method as generative “ensemble-regression” (GER), in analogy to the classic “point-regression,” where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Lévy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Comparison of inferred and observed interplanetary magnetic field polarities, 1970-1972

The inferred polarity (toward or away from the sun) of the interplanetary magnetic field at earth using polar observations of the geomagnetic field has been compared with spacecraft observations. A list published by Svalgaard (1974) of the inferred field polarities in the period from 1970 to 1972 is found to be correct on 82% of the days. A near real-time (same day) method of inferring the polarity of the interplanetary magnetic field using geomagnetic observations at Vostok and Thule is in use at the NOAA Space Environment Laboratory, Boulder, Colorado. During 1972, this method is found to be correct on 87% of the days. A list of 'well-defined' sector boundaries at earth from 1970 to 1972 is given.

Wilcox, J. M.↗