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At least 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Wilson loops with neural networks

Wilson loops are essential objects in QCD and have been pivotal in scale setting and demonstrating confinement. Various generalizations are crucial for computations needed in effective field theories. In lattice gauge theory, Wilson loop calculations face challenges, including excited-state contamination at short times and the signal-to-noise ratio issue at longer times. To address these problems, we develop a new method by using neural networks to parametrize interpolators for the static quark-antiquark pair. We construct gauge-equivariant layers for the network and train it to find the ground state of the system. The trained network itself is then treated as our new observable for the inference. Our results demonstrate a significant improvement in the signal compared to traditional Wilson loops, performing as well as Coulomb-gauge Wilson-line correlators while maintaining gauge invariance. Additionally, we present an example where the optimized ground state is used to measure the static force directly, as well as another example combining this method with the multilevel algorithm. Finally, we extend the formalism to find excited-state interpolators for static quark-antiquark systems. To our knowledge, this work is the first study of neural networks with a physically motivated loss function for Wilson loops.

Bellscheidt, Verena [Massachusetts Inst. of Techno↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Modeling pair distribution functions of rare-earth phosphate glasses using principal component analysis

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, co-doped with two rare-earth ions (R and R’) of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of co-doped REPGs presents significant challenges relative to their singly-doped counterparts; specifically, R and R’ are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown co-doped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are pre-validated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly-doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. Furthermore, while this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials, and be exploited in large-scale data-mining efforts that probe many t(r) functions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Value-of-Information Analysis of a Fracture Prediction Method

A core-based fracture prediction method is used to illustrate a Value of Information (VOI) decision-analysis protocol to inform completion decisions in tight-gas sandstones. The ratio of late host-rock cement to available pore volume or degradation index uses petrographic observations of cement distributions in core (including sidewall cores) to predict whether nearby but unsampled fractures (widths > 0.5 to 1 mm) are sealed (non-conductive) or open (conductive). Measurements from four sandstone plays suggest that the index correctly predicts open vs. sealed fractures with an accuracy in excess of 80%. The value added is calculated using Bayesian inference in which accuracy of the index serves as the likelihood of the prior distribution of open fractures to assess the posterior probability that data represent a useful predictor of producibility. VOI of the prediction method is more than three times the cost to acquire the data. VOI is most sensitive to play-specific geologic and cost parameters including cost to drill, expected revenue from a successful well, cost of completion, cost of acquiring data for the index, and fracture probability distributions. The approach provides a way to value acquiring fracture data and points to a need for zone-specific production data in tight-gas sandstones.

02 PETROLEUM↗
DOE OSTI2013

A field sampling strategy for semivariogram inference of fractures in rock outcrops

The stochastic continuum (SC) representation is one common approach for simulating the effects of fracture heterogeneity in groundwater flow and transport models. These SC reservoir models are generally developed using geostatistical methods (e.g., kriging or sequential simulation) that rely on the model semivariogram to describe the spatial variability of each continuum. Although a number of strategies for sampling spatial distributions have been published in the literature, little attention has been paid to the optimization of sampling in resource- or access-limited environments. Here we present a strategy for estimating the minimum sample spacing needed to define the spatial distribution of fractures on a vertical outcrop of basalt, located in the Box Canyon, east Snake River Plain, Idaho. We used fracture maps of similar basalts from the published literature to test experimentally the effects of different sample spacings on the resulting semivariogram model. Our final field sampling strategy was based on the lowest sample density that reproduced the semivariogram of the exhaustively sampled fracture map. Application of the derived sampling strategy to an outcrop in our field area gave excellent results, and illustrates the utility of this type of sample optimization. The method will work for developing a sampling plan for any intensive property, provided prior information for a similar domain is available; for example, fracture maps or ortho-rectified photographs from analogous rock types could be used to plan for sampling of a fractured rock outcrop.

58 GEOSCIENCES↗
DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
DOE OSTI2022

Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems

Abstract Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform physics-based models for systems with few observable degrees of freedom; e.g. hydrological systems. Here, we provide an operator-theoretic explanation for this empirical success. To predict a partially-observed system’s future behavior with physics-based models, the missing degrees of freedom must be explicitly accounted for using data assimilation and model parametrization. Data-driven models, in contrast, employ delay-coordinate embeddings and their evolution under the Koopman operator to implicitly model the effects of the missing degrees of freedom. We describe in detail the statistical physics of partial observations underlying data-driven models using novel maximum entropy and maximum caliber measures. The resulting nonequilibrium Wiener projections applied to the Mori–Zwanzig formalism reveal how data-driven models may converge to the true dynamics of the observable degrees of freedom. Additionally, this framework shows how data-driven models infer the effects of unobserved degrees of freedom implicitly, in much the same way that physics models infer the effects explicitly. This provides a unified implicit-explicit modeling framework for predicting partially-observed systems, with hybrid physics-informed machine learning methods combining both implicit and explicit aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The clustering of the SDSS-IV extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample: structure growth rate measurement from the anisotropic quasar power spectrum in the redshift range 0.8 < z < 2.2

We analyse the clustering of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 14 quasar sample (DR14Q).We measure the redshift space distortions using the power-spectrum monopole, quadrupole, and hexadecapole inferred from 148 659 quasars between redshifts 0.8 and 2.2, covering a total sky footprint of 2112.9 deg 2 . We constrain the logarithmic growth of structure times the amplitude of dark matter density fluctuations, fσ 8 , and the Alcock–Paczynski dilation scales that allow constraints to be placed on the angular diameter distance D A (z) and the Hubble H(z) parameter. At the effective redshift of z eff = 1.52, fσ 8 (z eff ) = 0.420 ± 0.076, H(z eff ) = [162 ± 12] (r$fid\atop{s}$ /r s ) kms -1 Mpc -1 , and DA(z eff ) = [1.85 ± 0.11] × 10 3 (r s/ r$fid\atop{s}$) Mpc, where r s is the comoving sound horizon at the baryon drag epoch and the superscript ‘fid’ stands for its fiducial value. The errors take into account the full error budget, including systematics and statistical contributions. These results are in full agreement with the current Λ-Cold Dark Matter cosmological model inferred from Planck measurements. Finally, we compare our measurements with other eBOSS companion papers and find excellent agreement, demonstrating the consistency and complementarity of the different methods used for analysing the data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

EXPLAINABLE AND TRUSTWORTHY DIAGNOSTICS ACHIEVABLE THROUGH PROCESS-BASED AUTOMATED REASONING

An approach has been developed that incorporates domain knowledge to obtain a more explainable and trustworthy equipment health monitoring diagnosis than might otherwise be obtained from a purely data-driven method. Physics-based models serve to constrain the realizable solution space and render a more trusted diagnosis. An automated reasoning algorithm performs backward chaining to infer a diagnosis that is consistent with logic statements that have been evaluated as true. This diagnosis is made explainable to an operator by providing the forward chaining path that elucidates for inspection and validity testing those truths implied by the diagnosis.

automated reasoning↗
NASA NTRS1971

Data acquisition and reduction for the U. Va. superconducting magnetic suspension and balance facility.

The problems associated with data acquisition and reduction in the University of Virginia superconducting magnetic suspension and balance facility are similar to those in free-flight ranges (or tunnels). The model undergoes a 'Quasi-six-degree-of-freedom' motion which must be monitored both in position and angular orientation from which the aerodynamics must be inferred. The data acquisition problem is made more difficult because geometric constraints prevent direct visual access to the model in the Mach 3 wind tunnel. The methods, accuracies and problems associated with the acquisition of data are discussed.-

Jacobson, I. D.↗
NASA NTRS1970

Data acquisition and reduction for the University of Virginia superconducting magnetic suspension and balance facility

The problems associated with data acquisition and reduction in the U. Va. superconducting magnetic suspension and balance facility are similar to those in free-flight ranges (or tunnels). The model undergoes a quasi-six-degree-of-freedom motion which must be monitored both in position and angular orientation from which the aerodynamics must be inferred. The data acquisition problem is made more difficult because geometric constraints prevent direct visual access to the model in the Mach 3 wind tunnel. The methods, accuracies, and problems associated with the acquisition of data are discussed.

Jacobson, I. D.↗
NASA NTRS1982

A technique for measuring magnetic fields on solar-type stars

A method is developed and tested by which magnetic-field strengths and area filling-factors of magnetic regions on late-type stars may be inferred from high-resolution, absorption-line profiles that have been broadened by the Zeeman effect. The technique involves fitting such profiles with a triplet of components, the shape of which is derived from profiles of lines of low Zeeman sensitivity. Tests of the systematic and random errors indicate that such magnetic flux measurements have an uncertainty of 20% for stars with field strengths of 2000 gauss if at least 10% of the stellar surface contains magnetic regions.

Marcy, G. W.↗
NASA NTRS1990

Relationship between the longwave cloud radiative forcing at the surface and the top of the atmosphere

In order to achieve global coverage, any surface radiation climatology has to be based on satellite observations. In the last decade several schemes have been devised to obtain the surface solar insolation from top of the atmosphere reflected solar radiation. More recently, attempts have been made to infer the components of longwave radiation at the surface from satellite sounder data using a radiative transfer model. In addition to the radiative transfer scheme, these methods require assumptions about the effective emitting temperature of cloud tops and bases. Modeling studies have shown that although there are strong correlations between the solar upwelling radiative flux and surface flux, this is not true of the longwave. However, if the clear sky component is considered separately such that the cloud longwave forcing at the top and at the surface are compared, a slightly different picture emerges. During the FIRE Cirrus IFO, surface radiation measurements were made at several sites and coincident satellite overpass data was also collected. It may be possible to extract the longwave cloud radiative forcing at the top and surface from these data. If relationships are verifiable by observations, this information can be useful for the extraction of the surface longwave radiation from satellite data. The radiative transfer schemes used to convert upwelling spectral radiances into a downwelling longwave radiation can provide the clear sky component. The cloud radiative forcing at the top of the atmosphere can then modify the surface fluxes according to relationships shown. It should be noted that this procedure may be considered only for temporal averages and not for instantaneous deductions of surface fluxes. This would be most useful in compiling monthly mean regional climatologies of the surface longwave fluxes.

HARSHVARDHAN↗
NASA NTRS1992

Planar laser-induced fluorescence measurements of high-enthalpy free jet flow with nitric oxide

Planar laser-induced fluorescence (PLIF) measurements of property fields in a high-enthalpy, supersonic, underexpanded free jet generated in a reflection-type shock tunnel are reported. PLIF images showing velocity and temperature sensitivity are presented. The inferred radial velocity and relative rotational temperature fields are found to be in agreement with those predicted by a numerical simulation of the flowfield using the method of characteristics.

Palmer, Jennifer L.↗
NASA NTRS1993

Total ozone and aerosol optical depths inferred from radiometric measurements in the Chappuis absorption band

A second-derivative smoothing technique, commonly used in inversion work, is applied to the problem of inferring total columnar ozone amounts and aerosol optical depths. The application is unique in that the unknowns may be solved for directly without employing standard inversion methods. It is shown, however, that by employing inversion constraints, better solutions are normally obtained. The method is quite versatile and able to deal with varying total ozone and various aerosol size distributions. The technique is applied first in simulation, then to 119 days of measurements taken in Tucson, Arizona, that are compared to TOMS values for the same dates. The technique is also applied to two measurements taken at Mauna Loa, Hawaii, for which Dobson ozone values are available in addition to the TOMS values, and the results agree to within 15 percent. It is also shown through simulations that additional information can be obtained from measurements outside the Chappuis band. This approach reduces the bias and spread of the estimated total ozone and is unique in that it uses measurements from both the Chappuis and Huggins absorption bands.

Flittner, D. E.↗
NASA NTRS2001

Inductive Measurement of Plasma Jet Electrical Conductivity (MSFC Center Director's discretionary Fund)

Measurement of plasma jet electrical conductivity has utility in the development of explosively driven magnetohydrodynamic (MHD) energy converters as well as magnetic flux compression reaction chambers for nuclear/chemical pulse propulsion and power. Within these types of reactors, the physical parameter of critical importance to underlying MHD processes is the magnetic Reynolds number, the value of which depends upon the product of plasma electrical conductivity and velocity. Therefore, a thorough understanding of MHD phenomena at high magnetic Reynolds number is essential, and methods are needed for the accurate and reliable measurement of electrical conductivity in high-speed plasma jets. It is well known that direct measurements using electrodes suffer from large surface resistance, and an electrodeless technique is desired. To address this need, an inductive probing scheme, originally developed for shock tube studies, has been adapted. In this method, the perturbation of an applied magnetic field by a plasma jet induces a voltage in a search coil, which, in turn, can be used to infer electrical conductivity through the inversion of a Fredholm integral equation of the first kind. A 1-in.-diameter probe using a light-gas gun. Exploratory laboratory experiments were carried out using plasma jets expelled from 15-g shaped charges. Measured conductivities were in the range of 4 kS/m for unseeded octol charges and 26 kS/m for seeded octol charges containing 2-percent potassium carbonate by mass.

Turner, M. W.↗
NASA NTRS2001

Properties of Stratospheric Aerosol Estimated from HALOE Data

Extinction coefficients for stratospheric aerosols at 8 HALOE (HALogen Occultation Experiment) wavelengths are determined by comparing transmittances data for two adjacent solar occultation measurements, where one limb path is loaded with aerosols but the other path is free of aerosols. These extinction coefficients are used to infer the aerosol properties such as composition and size distribution parameters. Mie theory has been used to calculate the extinction coefficients, and a nonlinear least square method is applied to determine the aerosol properties. Sixteen cases are selected for the retrieval in southern hemisphere at latitudes from 21 to 48 deg S for the period of 29 Mar - 31 May 1992. Retrieved size width ranges from 1.1 to 1.5 and radius ranges from 0.25 to 0.45 micrometers. These size parameters are within the ranges of in situ measurements at Laramie, Wyoming. Retrieved weight percent of H2SO4 is larger than the equilibrium value by about 5 to approximately 10 weight percent, similar to the results for northern hemisphere at latitudes 20 to 55 deg N for the period from Nov 1991 to Feb. 1992.

Lee, Kwang-Mog↗