Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “variational inference”

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
NASA NTRS1974

Comparison of atomic oxygen measurements by incoherent scatter and satellite-borne mass spectrometer techniques

Atomic oxygen densities determined by the incoherent scatter technique are compared to densities deduced from satellite-borne mass spectrometer measurements and are found to agree within experimental error. The diurnal variations inferred from the incoherent scatter measurements do show, however, some departure from diurnal variations found by modeling the mass spectrometer results. Some implications of these departures are briefly discussed.

Hedin, A. E.↗
DOE OSTI2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.

astronomy image processing↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Winter bulge and diurnal variations in hydrogen inferred from AE-C composition measurements

In situ mass spectrometric measurements of ion and neutral particle thermospheric compositions have been used to infer the latitudinal and diurnal variations of thermospheric atomic hydrogen for solstice conditions. Local time-dependent and local time-independent components of the observed hydrogen distribution were separated on the basis of a model generated by expanding the log of neutral hydrogen concentration in terms of spherical harmonics. Results are compared with analogous data on N2 concentration; the comparison reveals an anticorrelation between gas temperature and hydrogen concentration. The slope of this anticorrelation line represents the 'zero flux condition' from the exosphere theory of Hodges (1973), thus further bearing out the conclusion that exospheric flow is the dominant process governing the global distribution of hydrogen.

Brinton, H. C.↗
DOE OSTI2025

Accelerating Hamiltonian Monte Carlo for Bayesian inference in neural networks and neural operators

Hamiltonian Monte Carlo (HMC) is a powerful and accurate method to sample from the posterior distribution in Bayesian inference. However, HMC techniques are computationally demanding for Bayesian neural networks due to the high dimensionality of the network’s parameter space and the non-convexity of their posterior distributions. Therefore, various approximation techniques, such as variational inference (VI) or stochastic gradient MCMC, are often employed to infer the posterior distribution of the network parameters. Such approximations introduce inaccuracies in the inferred distributions, resulting in unreliable uncertainty estimates. In this work, we propose a hybrid approach that combines inexpensive VI and accurate HMC methods to efficiently and accurately quantify uncertainties in neural networks and neural operators. The proposed approach leverages an initial VI training on the full network. We examine the influence of individual parameters on the prediction uncertainty, which shows that a large proportion of the parameters do not contribute substantially to uncertainty in the network predictions. This information is then used to significantly reduce the dimension of the parameter space, and HMC is performed only for the subset of network parameters that strongly influence prediction uncertainties. This yields a framework for accelerating the full batch HMC for posterior inference in neural networks. We demonstrate the efficiency and accuracy of the proposed framework on deep neural networks and operator networks, showing that inference can be performed for large networks with tens to hundreds of thousands of parameters. Finally, we show that this method can effectively learn surrogates for complex physical systems by modeling the operator that maps from upstream conditions to wall-pressure data on a cone in hypersonic flow.

Bayesian inference↗
DOE OSTI2026

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters

In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more data needs to be collected. Bayesian Neural Networks (BNNs) produce predictive uncertainty by propagating uncertainty in neural network (NN) weights and offer the promise of obtaining not only an accurate predictive model but also accurate UQ. However, in practice, obtaining accurate UQ with BNNs is difficult due in part to the approximations used for model training (such as those made in variational inference) and in part to the need to choose a suitable set of hyperparameters; these hyperparameters outnumber those needed for traditional NNs and often have opaque effects on the results. We aim to shed light on the effects of hyperparameter choices for variational BNNs by performing a global sensitivity analysis of variational BNN performance under varying hyperparameter settings. Our results indicate that many of the hyperparameters interact with each other to affect both predictive accuracy and UQ. For improved usage of variational BNNs in real-world applications, we suggest that thorough hyperparameter tuning, including tuning of prior hyperparameters and loss function parameters, is essential for accurate UQ in variational BNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Structure-aware Initialization via Numerical Continuation and Informed Priors

Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.

Data integrity↗
DOE OSTI2025

Reducing model error using optimized galaxy selection: weak lensing cluster mass estimation

Galaxy clusters are one of the most powerful probes to study extensions of General Relativity and the Standard Cosmological Model. Upcoming surveys like the Vera Rubin Observatory’s Legacy Survey of Space and Time are expected to revolutionise the field, by enabling the analysis of cluster samples of unprecedented size and quality. To reach this era of high-precision cluster cosmology, the mitigation of sources of systematic error is crucial. A particularly important challenge is bias in cluster mass measurements induced by the mismodelling of photometric redshift estimates of source galaxies. This work proposes a method to optimise the source sample selection in cluster weak lensing analyses drawn from wide-field survey lensing catalogs to reduce the bias on reconstructed cluster masses. We use a combinatorial optimisation scheme and methods from variational inference to select galaxies in latent space to produce a probabilistic galaxy source sample catalog for highly accurate cluster mass estimation. We show that our method reduces the critical surface mass density Σ crit relative modelling bias on the 60-70% level, while maintaining up to 90% of galaxies. We highlight that our methodology has applications beyond cluster mass estimation as an approach to jointly combine galaxy selection and model inference under sources of systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Designing an Optimal Sensor Network via Minimizing Information Loss

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A scalable variational method for estimating the latent infection-rate field of an outbreak

In this paper, we explore whether the infection-rate of a disease can serve as a robust monitoring variable in epidemiological surveillance algorithms. The infection-rate is dependent on population mixing patterns that do not vary erratically day-to-day; in contrast, daily case-counts used in contemporary surveillance algorithms are corrupted by reporting errors. The technical challenge lies in estimating the latent infection-rate from case-counts. Here we devise a Bayesian method to estimate the infection-rate across multiple adjoining areal units, and then use it, via an anomaly detector, to discern a change in epidemiological dynamics. We extend an existing model for estimating the infection-rate in an areal unit by incorporating a Markov random field model, so that we may estimate infection-rates across multiple areal units, while preserving spatial correlations observed in the epidemiological dynamics. To carry out the high-dimensional Bayesian inverse problem, we develop an implementation of mean-field variational inference specific to the infection model and integrate it with the random field model to incorporate correlations across counties. The method is tested on estimating the COVID-19 infection-rates across all 33 counties in New Mexico using data from the summer of 2020, and then employing them to detect the arrival of the Fall 2020 COVID-19 wave. We perform the detection using a temporal algorithm that is applied county-by-county. We also show how the infection-rate field can be used to cluster counties with similar epidemiological dynamics.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1977

A global thermospheric model based on mass spectrometer and incoherent scatter data MSIS. II - Composition

Measurements of O, He, and Ar from neutral gas mass spectrometers on four satellites (Ogo 6, San Marco 3, Aeros A, and AEC-C) and inferred oxygen and hydrogen densities from an ion mass spectrometer on AE-C have been combined with a neutral temperature and nitrogen density model to produce a global model of thermospheric composition in terms of inferred variations at 120 km. The data set covers the time period from mid-1969 to mid-1975. The MSIS (mass spectrometer and incoherent scatter data) model is compared with the Ogo 6 model (Hedin et al., 1974). Ar variations at 120 km tend to be in phase with temperature variations and inverse to the He, O, and H variations.

Hedin, A. E.↗
NASA NTRS1985

Carbon and sulfur cycling through geologic time

Mathematical models of the coupled global systems of sedimentary reservoirs and fluxes are used to infer variations in reservoir sizes and rates of sedimentation over periods of hundreds of millions of years. Perhaps most interesting is the coupled sulfide/sulfate carbon/carbonate system that controls global oxygen and carbon dioxide production and consumption is discussed.

Garrels, R. M.↗
NASA NTRS1985

Small-scale convection beneath the transverse ranges, California: Implications for interpretation of gravity anomalies

Tomographic inversion of upper mantle P wave velocity heterogeneities beneath southern California shows two prominent features: an east-west trending curtain of high velocity material (up to 3% fast) in the upper 250 km beneath the Transverse Ranges and a region of low velocity material (up to 4% slow) in the 100 km beneath the Salton Trough. These seismic velocity anomalies were interpreted as due to small scale convection in the mantle. Using this hypothesis and assuming that temperature and density anomalies are linearly related to seismic velocity anomalies through standard coefficients of proportionality, leads to inferred variations of approx. + or - 300 C and approx. + or - 0.03 g/cc.

Humphreys, E. D.↗
NASA NTRS1985

Fractured toughness of Si3N4 measured with short bar chevron-notched specimens

The short bar chevron-notched specimen is used to measure the plane strain fracture toughness of hot pressed Si3N4. Specimen proportions and chevron-notch angle are varied, thereby varying the amount of crack extension to maximum load (upon which K sub IC is based). The measured toughness (4.68 + or - 0.19 MNm to the 3/2 power) is independent of these variations, inferring that the material has a flat crack growth resistance curve.

Salem, J. A.↗
NASA NTRS1987

Fracture toughness of Si3N4 measured with short bar chevron-notched specimens

The short bar chevron-notched specimen is used to measure the plane strain fracture toughness of hot pressed Si3N4. Specimen proportions and chevron-notch angle are varied, thereby varying the amount of crack extension to maximum load (upon which K sub IC is based). The measured toughness (4.68 + or 0.19 MNm to the 3/2 power) is independent of these variations, inferring that the material has a flat crack growth resistance curve.

Salem, Jonathan A.↗
NASA NTRS1992

Coronal abundances in solar active regions measured by the Solar Maximum Mission flat crystal spectrometer

High resolution soft X-ray spectra acquired by the Flat Crystal Spectrometer (FCS) on solar Maximum Mission provide an excellent data base to study the relative abundances of O, Ne, Mg, and Fe in solar active regions. The FCS data show significant variability for all combinations of these elements. The largest variation occurs for Fe:Ne, which shows region to region changes of up to a factor of 7, and frequent factor of 2 variations in day to day samples of a given region. The atomic data and the ionization balance calculations used to interpret the line ratios affect the actual abundance values obtained, but have little effect on the magnitude of the total range of variation inferred. Resonance scattering of Fe XVII could cause a systematic offset in the abundances determined, but cannot be responsbile for the bulk of the observed variability. While abundance variability complicates the derivation of plasma parameters from spectroscopic measurements, it should offer exciting new clues to the processes which form and heat the corona.

Saba, Julia L. R.↗
NASA NTRS2003

The Chemical Composition and Gas-to-Dust Mass Ratio of Nearby Interstellar Matter

We use recent results on interstellar gas toward nearby stars and interstellar by-products within the solar system to select among the equilibrium radiative transfer models of the nearest interstellar material presented in Slavin & Frisch. For the assumption that O/H - 400 parts per million, models 2 and 8 are found to yield good fits to available data on interstellar material inside and outside of the heliosphere, with the exception of the Ne abundance in the pickup ion and anomalous cosmic-ray populations. For these models, the interstellar medium (ISM) at the entry point to the heliosphere has n(H(sup 0)) = 0.202-0.208/cu cm, n(He(sup 0) = 0.0137-0.0152/cu cm, and ionizations X(H) = 0.29-0.30, X(He) = 0.47-0.51. These best models suggest that the chemical composition of the nearby ISM is approx.60%-70% subsolar if S is undepleted. Both H(0) and H(+) need to be included when evaluating abundances of ions found in warm diffuse clouds. Models 2 and 8 yield an H filtration factor of approx.0.46. Gas-to-dust mass ratios for the ISM toward epsilon CMa are R(sub gd) = 178-183 for solar abundances of Holweger or R(sub gd) = 611-657 for an interstellar abundance standard 70% solar. Direct observations of dust grains in the solar system by Ulysses and Galileo yield R(sub gd) appr0x. 115 for models 2 and 8, supporting earlier results (Frisch and coworkers). If the local ISM abundances are subsolar, then gas and dust are decoupled over small spatial scales. The inferred variation in R(sub gd) over parsec length scales is consistent with the fact that the ISM near the Sun is part of a dynamically active cluster of cloudlets flowing away from the Sco-Cen association. Observations toward stars within approx.500 pc show that R(sub gd) correlates with the percentage of the dust mass that is carried by iron, suggesting that an Fe-rich grain core (by mass) remains after grain destruction. Evidently large dust grains (>10(exp -13) g) and small dust grains (<10(exp -13) g) are not well mixed over parsec length spatial scales in the ISM. It also appears that very small C-dominated dust grains have been destroyed in the ISM within several parsecs of the Sun, since C appears to be essentially undepleted. However, if gas-dust coupling breaks down over the cloud lifetime, the missing mass arguments applied here to determine R(sub gd) and dust grain mineralogy are not appropriate.

Frisch, Priscilla C.↗