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DOE OSTI2024

Hutchinson Trace Estimation for high-dimensional and high-order Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data and physics. However, extending PINNs to high-dimensional and even high-order PDEs encounters significant challenges due to the computational cost associated with automatic differentiation in the residual loss function calculation. Herein, we address the limitations of PINNs in handling high-dimensional and high-order PDEs by introducing the Hutchinson Trace Estimation (HTE) method. Starting with the second-order high-dimensional PDEs, which are ubiquitous in scientific computing, HTE is applied to transform the calculation of the entire Hessian matrix into a Hessian vector product (HVP). This approach not only alleviates the computational bottleneck via Taylor-mode automatic differentiation but also significantly reduces memory consumption from the Hessian matrix to an HVP’s scalar output. We further showcase HTE’s convergence to the original PINN loss and its unbiased behavior under specific conditions. Comparisons with the Stochastic Dimension Gradient Descent (SDGD) highlight the distinct advantages of HTE, particularly in scenarios with significant variability and variance among dimensions. We further extend the application of HTE to higher-order and higher-dimensional PDEs, specifically addressing the biharmonic equation. By employing tensor-vector products (TVP), HTE efficiently computes the colossal tensor associated with the fourth-order high-dimensional biharmonic equation, saving memory and enabling rapid computation. The effectiveness of HTE is illustrated through experimental setups, demonstrating comparable convergence rates with SDGD under memory and speed constraints. Additionally, HTE proves valuable in accelerating the Gradient-Enhanced PINN (gPINN) version as well as the Biharmonic equation. Overall, HTE opens up a new capability in scientific machine learning for tackling high-order and high-dimensional PDEs.

Curse of dimensionality↗

DOE OSTI2022

FY22 Progress on Multicontinuum Methods in Containment

Estimation of two-phase fluid flow properties is important to understand and predict water and gas movement through the vadose zone for agricultural, hydrogeological, and engineering applications, such as containment transport and/or containment of gases in the subsurface. To estimate rock fluid flow properties and subsequently predict physically realistic processes such as patterns and timing of water, gas, and energy (e.g., heat) movement in the subsurface, laboratory spontaneous water imbibition with simultaneous temperature measurement and numerical modeling methods are presented in the FY22 progress report. A multiple-overlapping-continua conceptual model is used to explain and predict observed complex multi-phenomenological laboratory test behavior during spontaneous imbibition experiments. This report primarily addresses two complexities that arise during the experiments: 1) capturing the late-time behavior of spontaneous imbibition tests with dual porosity; and 2) understanding the thermal perturbation observed at or ahead of the imbibing wetting front, which are associated with adsorption of water in initially dry samples. We use numerical approaches to explore some of these issues, but also lay out a plan for further laboratory experimentation and modeling to best understand and leverage these unique observations.

58 GEOSCIENCES↗

DOE OSTI2024

Reweighting Monte Carlo predictions and automated fragmentation variations in Pythia 8

This work reports on a method for uncertainty estimation in simulated collider-event predictions. The method is based on a Monte Carlo-veto algorithm, and extends previous work on uncertainty estimates in parton showers by including uncertainty estimates for the Lund string-fragmentation model. This method is advantageous from the perspective of simulation costs: a single ensemble of generated events can be reinterpreted as though it was obtained using a different set of input parameters, where each event now is accompanied with a corresponding weight. This allows for a robust exploration of the uncertainties arising from the choice of input model parameters, without the need to rerun full simulation pipelines for each input parameter choice. Such explorations are important when determining the sensitivities of precision physics measurements. Accompanying code is available at https://gitlab.com/uchep/mlhad-weights-validation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

BEYONDPLANCK VI. Noise characterization and modeling

We present a Bayesian method for estimating instrumental noise parameters and propagating noise uncertainties within the global BeyondPlanck Gibbs sampling framework, and apply this to Planck Low Frequency Instrument (LFI) time-ordered data. Following previous literature, we initially adopt a 1/ f model for the noise power spectral density (PSD), but find the need for an additional lognormal component in the noise model for the 30 and 44 GHz bands. We implement an optimal Wiener-filter (or constrained realization) gap-filling procedure to account for masked data. We then use this procedure to both estimate the gapless correlated noise in the time-domain, n corr , and to sample the noise PSD parameters, ξ n = {σ 0 , f knee , α, A p }. In contrast to previous Planck analyses, we assume piecewise stationary noise only within each pointing period (PID), not throughout the full mission, but we adopt the LFI Data Processing Center (DPC) results as priors on α and f knee . On average, we find best-fit correlated noise parameters that are mostly consistent with previous results, with a few notable exceptions. However, a detailed inspection of the time-dependent results reveals many important findings. First and foremost, we find strong evidence for statistically significant temporal variations in all noise PSD parameters, many of which are directly correlated with satellite housekeeping data. Second, while the simple 1/ f model appears to be an excellent fit for the LFI 70 GHz channel, there is evidence for additional correlated noise not described by a 1/ f model in the 30 and 44 GHz channels, including within the primary science frequency range of 0.1–1 Hz. In general, most 30 and 44 GHz channels exhibit deviations from 1/ f at the 2–3σ level in each one hour pointing period, motivating the addition of the lognormal noise component for these bands. For some periods of time, we also find evidence of strong common mode noise fluctuations across the entire focal plane. Overall, we conclude that a simple 1/ f profile is not adequate to fully characterize the Planck LFI noise, even when fitted hour-by-hour, and a more general model is required. These findings have important implications for large-scale CMB polarization reconstruction with the Planck LFI data, and the current work is a first attempt at understanding and mitigating these issues.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1973

Visual estimates of brightness and analysis of observations of variable stars

Methods of visual estimation of the brightness of variable stars include the Argelander method, Pickering method, and Neyland-Blazhko method. Systematic errors are discussed together with methods of plotting brightness curves.

Tsesevich, V. P.↗

DOE OSTI2018

Measures of model performance based on the log accuracy ratio

Quantitative assessment of modeling and forecasting of continuous quantities uses a variety of approaches. We review existing literature describing metrics for forecast accuracy and bias, concentrating on those based on relative errors and percentage errors. Of these accuracy metrics, the mean absolute percentage error (MAPE) is one of the most common across many fields and has been widely applied in recent space science literature and we highlight the benefits and drawbacks of MAPE and proposed alternatives. We then introduce the log accuracy ratio, and derive from it two metrics: the median symmetric accuracy; and the symmetric signed percentage bias. Robust methods for estimating the spread of a multiplicative linear model using the log accuracy ratio are also presented. The developed metrics are shown to be easy to interpret, robust, and to mitigate the key drawbacks of their more widely-used counterparts based on relative errors and percentage errors. Their use is illustrated with radiation belt electron flux modeling examples.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Computational Framework to Accelerate the Discovery of Perovskites for Solar Thermochemical Hydrogen Production: Identification of Gd Perovskite Oxide Redox Mediators

A high-throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd-containing perovskite oxide compositions Gd 2 BB'O 6 , GdA'B 2 O 6 , and GdA'BB'O 6 that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH TR and ΔS TR . This scheme uses the machine-learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO 6 octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic- and defect-sampling to predict STCH-relevant properties. Eighty-three promising STCH candidate perovskite oxides down-selected from 4392 Gd-containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. Our results demonstrate that the high-throughput computational scheme described herein—which is used to evaluate Gd-containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Plasma GFAP for populational enrichment of clinical trials in preclinical Alzheimer's disease

Abstract INTRODUCTION Cognitively unimpaired (CU) amyloid beta (Aβ)+ individuals with elevated plasma glial fibrillary acidic protein (GFAP) have an increased risk of Alzheimer's disease (AD)‐related progression. We tested the utility of plasma GFAP for population enrichment CU populations in clinical trials. METHODS We estimated longitudinal progression, effect size, and costs of hypothetical clinical trials designed to test an estimated 25% drug effect on reducing tau positron emission tomography (PET) accumulation in the medial temporal lobe (MTL) and temporal neocortical region (NEO‐T). RESULTS CU GFAP+/Aβ+ individuals present an increased annual rate of change and effect size in tau PET MTL and tau PET NEO‐T compared to the other groups. An enrichment strategy selecting CU GFAP+/Aβ+ individuals would require a smaller sample size (≈ 57% reduction) and fewer Aβ PET scans (≈ 74% reduction) than trials enriched with Aβ PET alone, reducing total clinical trial costs by up to 64%. DISCUSSION Our results suggest that clinical trials focusing on preclinical AD recruiting Aβ+ individuals with elevated GFAP levels would improve cost effectiveness. Highlights Cognitively unimpaired (CU) glial fibrillary acidic protein (GFAP)+/amyloid beta (Aβ)+ shows increased changes in tau positron emission tomography (PET) . CU GFAP+/Aβ+ enriched clinical trials require a reduced sample size compared to Aβ+ only. CU GFAP+/Aβ+ enrichment reduces Aβ PET scans required and costs. CU GFAP+/Aβ+ enrichment allows the selection of individuals at early stages of the Alzheimer's disease continuum.

Neurosciences & Neurology↗

DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗

DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Storm-driven particulate organic matter flux connects a tidal tributary floodplain wetland, mainstem river, and estuary

The transport of terrestrial plant matter into coastal waters is important to regional and global biogeochemical cycles, and methods for assessing and predicting fluxes in such dynamic environments are needed. We investigated the hypothesis that upon reconnection of a floodplain wetland to its mainstem river, organic matter produced in the wetland would reach other parts of the ecosystem. If so, we can infer that the organic matter would ultimately become a source for the food web in the mainstem river and estuary. To accomplish this, we adapted numerical hydrodynamic and transport modeling methods to estimate the mass of particulate organic matter (POM) derived from the annually senescent aboveground parts of herbaceous marsh plants (H–POM). The Finite–Volume Community Ocean Model (FVCOM), parameterized with flow, tide, and aboveground biomass data, simulated H–POM mobilization from fluid shear stress during tidal exchange, flooding, and variable river flow; entrainment into the water column; transport via channel and overland flow; and entrapment when wetted surfaces dry. We examined export from a recently reconnected, restoring tidal emergent marsh on the Grays River, a tributary to the Columbia River estuary. Modeling indicated that hydrologically reconnecting 65 ha at the site resulted in export of about 96 × 10 3 kg of H–POM, primarily during pulsed storm flooding events in autumn and early winter. This exported mass amounted to about 19% of the summer peak aboveground biomass measured at the site. Of that 19%, about 48% (47 × 10 3 kg) was deposited downstream in the Grays River and floodplain wetlands, and the remaining 52% (50 × 10 3 kg) passed the confluence of the Grays River and the mainstem estuary located about 7 km from the study site. The colonization of the restoring study site largely by nonnative Phalaris arundinacea (reed canarygrass) may have resulted in 18–28% lower H–POM mobilization than typical marsh plant communities on this floodplain, based on estimates from regional studies of marshes dominated by less recalcitrant species. Here, we concluded that restored floodplain wetlands can contribute significant amounts of organic matter to the estuarine ecosystem and thereby contribute to the restoration of historical trophic structure.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Evolutionary history, potential intermediate animal host, and cross-species analyses of SARS-CoV-2

To investigate the evolutionary history of the recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China, a total of 70 genomes of virus strains from China and elsewhere with sampling dates between 24 December 2019 and 3 February 2020 were analyzed. To explore the potential intermediate animal host of the SARS-CoV-2 virus, we reanalyzed virome data sets from pangolins and representative SARS-related coronaviruses isolates from bats, with particular attention paid to the spike glycoprotein gene. We performed phylogenetic, split network, transmission network, likelihood-mapping, and comparative analyses of the genomes. Additionally, based on Bayesian time-scaled phylogenetic analysis using the tip-dating method, we estimated the time to the most recent common ancestor and evolutionary rate of SARS-CoV-2, which ranged from 22 to 24 November 2019 and 1.19 to 1.31 × 10 -3 substitutions per site per year, respectively. Our results also revealed that the BetaCoV/bat/Yunnan/RaTG13/2013 virus was more similar to the SARS-CoV-2 virus than the coronavirus obtained from the two pangolin samples (SRR10168377 and SRR10168378). We also identified a unique peptide (PRRA) insertion in the human SARS-CoV-2 virus, which may be involved in the proteolytic cleavage of the spike protein by cellular proteases, and thus could impact host range and transmissibility. Interestingly, the coronavirus carried by pangolins did not have the RRAR motif. Therefore, we concluded that the human SARS-CoV-2 virus, which is responsible for the recent outbreak of COVID-19, did not come directly from pangolins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Surface analysis insight note: Synthetic line shapes, integration regions and relative sensitivity factors

Here, methods for estimating photoemission intensity from X-ray photoelectron spectroscopy data are examined. The role played by “synthetic” bell-shaped curves, integration intervals, background curves, and the use of relative sensitivity factors (RSFs) in reporting percentage atomic concentration for a sample is presented. In particular, photoemission lines with differing energy distributions obtained from the NaCl sample surface are used to demonstrate how a comparison of photoemission intensities is dependent on the line shapes, background curves, and appropriate use of RSFs.

42 ENGINEERING↗

DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Federated Learning with Frequency Estimation for Smart Meter Systems

Federated learning (FL) is a powerful framework that enables multiple distributed clients to collaborate without the need to transfer their data to a central server. However, FL does not inherently guarantee the level of privacy that clients often require. In our review of recent studies on privacy-enhancing techniques in FL, we found that frequency estimation (FE) methods remain underexplored. To address this gap, we developed and integrated FE techniques on the client side, further examining the effects of incorporating an adaptive range and a shuffled model. We also analyzed the impact of varying hyper-parameters on privacy preservation. Our results provide clear guidance on the algorithms and configurations that are most effective for enhancing privacy in FL, particularly when using long short-term memory (LSTM) architectures.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

DOE OSTI2025

Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Exploiting stochastic locality in lattice QCD: hadronic observables and their uncertainties

Abstract Because of the mass gap, lattice QCD simulations exhibit stochastic locality: distant regions of the lattice fluctuate independently. There is a long history of exploiting this to increase statistics by obtaining multiple spatially-separated samples from each gauge field; in the extreme case, we arrive at the master-field approach in which a single gauge field is used. Here we develop techniques for studying hadronic observables using position-space correlators, which are more localized, and compare with the standard time-momentum representation. We also adapt methods for estimating the variance of an observable from autocorrelated Monte Carlo samples to the case of correlated spatially-separated samples.

Physics↗

DOE OSTI2022

Tissue Distribution of Mercury in the Bodies of Wild American Alligators ( Alligator mississippiensis ) from a Coastal Marsh in Louisiana (USA)

Total mercury (THg) concentrations were measured in wild alligators inhabiting a coastal marsh in southern Louisiana, to determine the tissue distribution of THg among various body organs and tissue compartments. Concentrations of THg in claws and dermal tail scutes were compared to those in blood, brain, gonad, heart, kidney, liver, and skeletal muscle to determine if the former tissues, commonly available by non-lethal sampling, could be used as measures of body burdens in various internal organs. Mercury was found in all body organs and tissue compartments. However, overall, THg concentrations measured in alligators were below the FDA action level for fish consumption and were comparable to previous data reported from southwestern Louisiana. Here, our results suggest consumption of meat from alligators found in this region may be of little public health concern. However, the extended period of time between sampling (in this study) and the present-day highlight the need for continuous, additional, and more recent sampling to ensure consumer safety. Total mercury concentrations were highest in the kidney (3.18 ± 0.69 mg/kg dw) and liver (3.12 ± 0.76 mg/kg dw). THg levels in non-lethal samples (blood, claws, and dermal tail scutes) were positively correlated with all tissue THg concentrations (blood: R 2 = 0.513 – 0.988; claw: R 2 = 0.347 – 0.637, scutes: R 2 = 0.333 – 0.649). Because THg concentrations from blood, claws, and scutes were correlated with those of the internal organs, non-lethal sampling methods may be a viable method of estimating levels of THg in other body tissues.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗