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DOE OSTI2022

Dynamic Matrix Completion Based State Estimation in Distribution Grids

The power distribution network is undergoing tremendous transformation due to an increase in the penetration of renewable energy resources and electric vehicles. These changes have resulted in greater uncertainty and dynamics in the distribution grid states. Therefore, the ability to track and monitor system states has become a critical need for accurate and timely control actions. In this paper, we propose two dynamic sparsity-based state estimation approaches for distribution systems: (1) locally weighted matrix completion (LW-MC) and (2) Bayesian matrix completion with Kalman filter prediction (BMC-KF). The performance of the proposed dynamic state estimation strategies is compared with the classic/static matrix completion (static-MC) approach using the IEEE 37 and IEEE 123 bus test systems. Finally, results indicate that BMC-KF approach outperforms both LW-MC as well as static-MC even when 30% of the measurement data is available. Computational complexity associated with both approaches is quantified.

42 ENGINEERING↗

DOE OSTI2026

Supercharging simulation-based inference for Bayesian optimal experimental design

Abstract Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods are able to match or outperform by up to 22% existing state-of-the-art approaches across standard BOED benchmarks.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2003

Modeling the Absorbing Aerosol Index

We propose a scheme to model the absorbing aerosol index and improve the biomass carbon inventories by optimizing the difference between TOMS aerosol index (AI) and modeled AI with an inverse model. Two absorbing aerosol types are considered, including biomass carbon and mineral dust. A priori biomass carbon source was generated by Liousse et al [1996]. Mineral dust emission is parameterized according to surface wind and soil moisture using the method developed by Ginoux [2000]. In this initial study, the coupled CCM1 and GRANTOUR model was used to determine the aerosol spatial and temporal distribution. With modeled aerosol concentrations and optical properties, we calculate the radiance at the top of the atmosphere at 340 nm and 380 nm with a radiative transfer model. The contrast of radiance at these two wavelengths will be used to calculate AI. Then we compare the modeled AI with TOMS AI. This paper reports our initial modeling for AI and its comparison with TOMS Nimbus 7 AI. For our follow-on project we will model the global AI with aerosol spatial and temporal distribution recomputed from the IMPACT model and DAO GEOS-1 meteorology fields. Then we will build an inverse model, which applies a Bayesian inverse technique to optimize the agreement of between model and observational data. The inverse model will tune the biomass burning source strength to reduce the difference between modelled AI and TOMS AI. Further simulations with a posteriori biomass carbon sources from the inverse model will be carried out. Results will be compared to available observations such as surface concentration and aerosol optical depth.

Penner, Joyce↗

DOE OSTI2026

Efficient Optimization of Plasma Radiation Detector Configurations using Imperfect Inference Models

The configurations of instruments fielded on an experiment affect the amount of information captured and the quality of subsequent inference. Here, we investigate the problem of optimizing plasma x-ray radiation detectors in a magneto-inertial fusion experiment at Sandia National Laboratories. It is impossible to directly measure properties such as the temperature of the thermonuclear fusion plasma produced in these experiments because of the extreme environment and destructive nature of the experiment. Among other diagnostics, several detectors are placed with significant standoff from the fusion target to capture the x-rays emitted by the fusion plasma, which can be used to infer some of its properties. To optimize the configuration of these detectors, a high-fidelity model (HFM) is used for simulating outputs and a low-fidelity model (LFM) is used for inference. We develop methods based on A- and L-optimality criteria that are efficient to compute while explicitly accounting for the discrepancy between the HFM and the LFM. The method allows us to find detector configurations that perform similarly to or better than the configuration obtained using an existing sampling-based optimization method while decreasing computational time by a factor of 50. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian optimization↗

DOE OSTI2020

Quantifying uncertainties and correlations in the nuclear-matter equation of state

We perform statistically rigorous uncertainty quantification (UQ) for chiral effective field theory (χ EFT) applied to infinite nuclear matter up to twice nuclear saturation density. The equation of state (EOS) is based on high-order many-body perturbation theory calculations with nucleon-nucleon and three-nucleon interactions up to fourth order in the χ EFT expansion. From these calculations our newly developed Bayesian machine-learning approach extracts the size and smoothness properties of the correlated EFT truncation error. Furthermore, we then propose a novel extension that uses multitask machine learning to reveal correlations between the EOS at different proton fractions. The inferred in-medium χ EFT breakdown scale in pure neutron matter and symmetric nuclear matter is consistent with that from free-space nucleon-nucleon scattering. These significant advances allow us to provide posterior distributions for the nuclear saturation point and propagate theoretical uncertainties to derived quantities: the pressure and incompressibility of symmetric nuclear matter, the nuclear symmetry energy, and its derivative. Our results, which are validated by statistical diagnostics, demonstrate that an understanding of truncation-error correlations between different densities and different observables is crucial for reliable UQ. The methods developed here are publicly available as annotated Jupyter notebooks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Scalar bounded-from-below conditions from Bayesian active learning

We present a procedure leveraging Bayesian deep active learning to rapidly produce highly accurate approximate bounded-from-below conditions for arbitrary renormalizable scalar potentials, in the form of a neural network which may be saved and exported for use in arbitrary parameter space scans. We explore the performance of our procedure on three different scalar potentials with either highly nontrivial or unknown symbolic bounded-from-below conditions (the most general two-Higgs doublet model, the three-Higgs doublet model, and a version of the Georgi-Machacek model without custodial symmetry). We find that we can produce fast and highly accurate binary classifiers for all three potentials. Furthermore, for the potentials for which no known symbolic necessary and sufficient conditions on boundedness-from-below exist, our classifiers substantially outperform some common approximate analytical methods, such as producing tractable sufficient but not necessary conditions or evaluating boundedness-from-below conditions for scenarios in which only a subset of the theory’s fields achieve vacuum expectation values. Our methodology can be readily adapted to any renormalizable scalar field theory. For the community’s use, we have developed a package, BFBrain, which allows for the rapid implementation of our analysis procedure on user-specified scalar potentials with a high degree of customizability. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2018

A Likely Detection of a Two-planet System in a Low-magnification Microlensing Event

We report on the analysis of a microlensing event, OGLE-2014-BLG-1722, that showed two distinct short-term anomalies. The best-fit model to the observed light curves shows that the two anomalies are explained with two planetary mass ratio companions to the primary lens. Although a binary-source model is also able to explain the second anomaly, it is marginally ruled out by 3.1σ. The two-planet model indicates that the first anomaly was caused by planet “b” with a mass ratio of q=(4.5{sub −0.6}{sup +0.7})×10{sup −4} and projected separation in units of the Einstein radius, s = 0.753 ± 0.004. The second anomaly reveals planet “c” with a mass ratio of q{sub 2}=(7.0{sub −1.7}{sup +2.3})×10{sup −4} with Δχ {sup 2} ∼ 170 compared to the single-planet model. Its separation has two degenerated solutions: the separation of planet c is s {sub 2} = 0.84 ± 0.03 and 1.37 ± 0.04 for the close and wide models, respectively. Unfortunately, this event does not show clear finite-source and microlensing parallax effects; thus, we estimated the physical parameters of the lens system from Bayesian analysis. This gives the masses of planets b and c as m{sub b}=56{sub −33}{sup +51} and m{sub c}=85{sub −51}{sup +86} M{sub ⊕}, respectively, and they orbit a late-type star with a mass of M{sub host} =0.40{sub −0.24}{sup +0.36} M{sub ⊙} located at D{sub L}=6.4{sub −1.8}{sup +1.3} kpc from us. The projected distances between the host and planets are r{sub ⊥,b}=1.5±0.6 au for planet b and r{sub ⊥,c}=1.7{sub −0.6}{sup +0.7} au and r{sub ⊥,c}=2.7{sub −1.0}{sup +1.1} au for the close and wide models of planet c. If the two-planet model is true, then this is the third multiple-planet system detected using the microlensing method and the first multiple-planet system detected in low-magnification events, which are dominant in the microlensing survey data. The occurrence rate of multiple cold gas giant systems is estimated using the two such detections and a simple extrapolation of the survey sensitivity of the 6 yr MOA microlensing survey combined with the 4 yr μFUN detection efficiency. It is estimated that 6% ± 2% of stars host two cold giant planets.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Measuring the Hubble Constant with Dark Neutron Star–Black Hole Mergers

Abstract Detection of gravitational waves (GWs) from neutron star-black hole (NSBH) standard sirens provides local measurements of the Hubble constant (H 0 ), regardless of the detection of an electromagnetic (EM) counterpart, given that matter effects can be exploited to break the redshift degeneracy of the GW waveforms. The distinctive merger morphology and the high-redshift detectability of tidally disrupted NSBH make them promising candidates for this method. Also, the detection prospects of an EM counterpart for these systems will be limited toz< 0.8 in the optical, in the era of future GW detectors. Using recent constraints on the equation of state of NSs from multi-messenger observations of NICER and LIGO/Virgo/KAGRA, we show the prospects of measuringH 0 solely from GW observation of NSBH systems, achievable by the Einstein telescope (ET) and Cosmic Explorer (CE) detectors. We first analyze individual events to quantify the effect of high-frequency (≥500 Hz) tidal distortions on the inference of NS tidal deformability parameter (Λ) and hence onH 0 . We find that disruptive mergers can constrain Λ up to  ( 60 % ) more precisely than nondisruptive ones. However, this precision is not sufficient to place stringent constraints on theH 0 from individual events. By performing Bayesian analysis on simulated NSBH data (up toN= 100 events, corresponding to a day of observation) in the ET+CE detectors, we find that NSBH systems enable unbiased 4%–13% precision on the estimate ofH 0 (68% credible interval). This is a similar measurement precision found in studies analyzing NSBH mergers with EM counterparts in the LVKC O5 era.

Astronomy & Astrophysics↗

DOE OSTI2023

Arctic Cloud‐Base Ice Precipitation Properties Retrieved Using Bayesian Inference

Abstract Cloud‐climate feedbacks are still the greatest source of uncertainty in current climate projections. Arctic clouds, which are predominantly stratiform and supercooled, often long‐lived, and nearly continuously precipitate ice particles, contribute roughly 10% of the uncertainty attributed to the global cloud feedback. This Arctic cloud uncertainty is driven by incomplete observational and theoretical knowledge required to estimate and explain the state and active processes occurring in those clouds. A focus on ice precipitation properties at Arctic cloud base rather than the surface deconfounds the product of cloud condensate sink processes from the influence of the atmospheric thermodynamic state below cloud base, rendering cloud‐base properties a more appealing target for inference and evaluation of model simulations. Here I describe an inverse model for the estimation of cloud base ice precipitation properties over Utqiagvik, North Slope of Alaska, using the synthesis of ground‐based radar and lidar measurements. By leveraging a Markov Chain Monte Carlo algorithm as the core of the inverse model, a wide range of particle size distributions are sampled, and different combinations of ice habit models are examined, both of which are typically fixed in other retrieval methods. Results show intriguing links between different cloud base thermodynamic and ice precipitation properties. Apparent ice number concentration enhancements at temperatures of −5 and −15°C suggest possible secondary ice production (SIP). The analysis alludes to an overestimation of SIP occurrence and intensity, especially in studies relying only on radar or lidar measurements. Finally, reflectivity‐dependent ice precipitation rate and ice water content parameterizations are presented.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Xylem-dwelling pathogen unaffected by local xylem vessel network properties in grapevines ( Vitis spp.)

Abstract Background and aims Xylella fastidiosa (Xf) is the xylem-dwelling bacterium associated with Pierce’s disease (PD), which causes mortality in agriculturally important species, such as grapevine (Vitis vinifera). The development of PD symptoms in grapevines depends on the ability of Xf to produce cell-wall-degrading enzymes to break up intervessel pit membranes and systematically spread through the xylem vessel network. Our objective here was to investigate whether PD resistance could be mechanistically linked to xylem vessel network local connectivity. Methods We used high-resolution X-ray micro-computed tomography (microCT) imaging to identify and describe the type, area and spatial distribution of intervessel connections for six different grapevine genotypes from three genetic backgrounds, with varying resistance to PD (four PD resistant and two PD susceptible). Key results Our results suggest that PD resistance is unlikely to derive from local xylem network connectivity. The intervessel pit area (Ai) varied from 0.07 ± 0.01 mm2 mm−3 in Lenoir to 0.17 ± 0.03 mm2 mm−3 in Blanc do Bois, both PD resistant. Intervessel contact fraction (Cp) was not statically significant, but the two PD-susceptible genotypes, Syrah (0.056 ± 0.015) and Chardonnay (0.041 ± 0.013), were among the most highly connected vessel networks. Neither Ai nor Cp explained differences in PD resistance among the six genotypes. Bayesian re-analysis of our data shows moderate evidence against the effects of the traits analysed: Ai (BF01 = 4.88), mean vessel density (4.86), relay diameter (4.30), relay density (3.31) and solitary vessel proportion (3.19). Conclusions Our results show that radial and tangential xylem network connectivity is highly conserved within the six different Vitis genotypes we sampled. The way that Xf traverses the vessel network may limit the importance of local network properties to its spread and may confer greater importance on host biochemical responses.

Plant Sciences↗

DOE OSTI2021

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

In this work, we introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system's behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Uncertainty Analysis in 3D Equilibrium Reconstruction

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the results of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Bayesian Estimation of the D(p,γ) 3 He Thermonuclear Reaction Rate

Big bang nucleosynthesis (BBN) is the standard model theory for the production of the light nuclides during the early stages of the universe, taking place for a period of about 20 minutes after the big bang. Deuterium production, in particular, is highly sensitive to the primordial baryon density and the number of neutrino species, and its abundance serves as a sensitive test for the conditions in the early universe. The comparison of observed deuterium abundances with predicted ones requires reliable knowledge of the relevant thermonuclear reaction rates, and their corresponding uncertainties. Recent observations reported the primordial deuterium abundance with percent accuracy, but some theoretical predictions based on BBN are at tension with the measured values because of uncertainties in the cross section of the deuterium-burning reactions. In this work, we analyze the S-factor of the D(p,γ) 3 He reaction using a hierarchical Bayesian model. We take into account the results of eleven experiments, spanning the period of 1955--2021; more than any other study. We also present results for two different fitting functions, a two-parameter function based on microscopic nuclear theory and a four-parameter polynomial. Furthermore, our recommended reaction rates have a 2.2\% uncertainty at 0.8~GK, which is the temperature most important for deuterium BBN. Differences between our rates and previous results are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A compact x-ray spectrometer for measurements of electron temperature distributions in inertial confinement fusion implosions at OMEGA

The Wedge Range Filter (WRF), commonly used for proton spectroscopy at the OMEGA Laser Facility and National Ignition Facility, is adapted to measure the x-ray continuum spectrum through transmission measurement using a continuous-gradient filter. Continuum x rays emitted from the hotspot of an implosion contain information about the plasma composition and electron temperature. The WRF data are leveraged to probe this distribution, specifically the electron temperature distribution. In this work, the data recorded with the WRF are forward modeled using a temperature distribution model folded with the WRF response function. An uncertainty analysis is conducted through a Bayesian regression algorithm using a Hamiltonian Monte Carlo sampler. This analysis enables the uncertainties in the instrument response to be folded into the uncertainty estimation of the electron temperature and absolute x-ray emission. Data analysis for a series of OMEGA implosions is presented and compared with radiation hydrodynamic simulations.

Lasers↗

DOE OSTI2016

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

58 GEOSCIENCES↗

NASA NTRS2021

KMT-2021-BLG-0322: Severe Degeneracy Between Triple-lens and Higher-order Binary-lens Interpretations

We investigate the microlensing event KMT-2021-BLG-0322, for which the light curve exhibits three distinctive sets of caustic-crossing features. It is found that the overall features of the light curve are approximately described by a binary-lens (2L1S) model, but the model leaves substantial residuals. We test various interpretations with the aim of explaining the residuals. Methods. We find that the residuals can be explained either by considering a nonrectilinear lens-source motion caused by the microlens-parallax and lens-orbital effects or by adding a low-mass companion to the binary lens (3L1S model). The degeneracy between the higher-order 2L1S model and the 3L1S model is very severe, making it difficult to single out a correct solution based on the photometric data. This degeneracy was known before for two previous events (MACHO-97-BLG-41 and OGLE-2013-BLG-0723),which led to the false detections of planets in binary systems, and thus the identification of the degeneracy for KMT-2021-BLG-0322illustrates that the degeneracy can be not only common but also very severe, emphasizing the need to check both interpretations of deviations from 2L1S models. Results. From the Bayesian analysis conducted with the measured lensing observables of the event timescale, angular Einstein radius, and microlens parallax, it was estimated that the binary lens components have masses(M1,M2)=(0.62+0.25−0.26M,0.07+0.03−0.03M), for both2L1S and 3L1S solutions, and the mass of the tertiary lens component according to the 3L1S solution isM3=6.40+2.64−2.78MJ.

Gravitational lensing: micro↗

NASA NTRS2014

Statistical Symbolic Execution with Informed Sampling

Symbolic execution techniques have been proposed recently for the probabilistic analysis of programs. These techniques seek to quantify the likelihood of reaching program events of interest, e.g., assert violations. They have many promising applications but have scalability issues due to high computational demand. To address this challenge, we propose a statistical symbolic execution technique that performs Monte Carlo sampling of the symbolic program paths and uses the obtained information for Bayesian estimation and hypothesis testing with respect to the probability of reaching the target events. To speed up the convergence of the statistical analysis, we propose Informed Sampling, an iterative symbolic execution that first explores the paths that have high statistical significance, prunes them from the state space and guides the execution towards less likely paths. The technique combines Bayesian estimation with a partial exact analysis for the pruned paths leading to provably improved convergence of the statistical analysis. We have implemented statistical symbolic execution with in- formed sampling in the Symbolic PathFinder tool. We show experimentally that the informed sampling obtains more precise results and converges faster than a purely statistical analysis and may also be more efficient than an exact symbolic analysis. When the latter does not terminate symbolic execution with informed sampling can give meaningful results under the same time and memory limits.

Reliability↗