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DOE OSTI2023

Information encoding in the spatial correlations of entangled twin beams

The ability to use the temporal and spatial degrees of freedom of quantum states of light to encode and transmit information is crucial for a robust and efficient quantum network. In particular, the potential offered by the large dimensionality of the spatial degree of freedom remains unfulfilled, as the necessary level of control required to encode information remains elusive. We encode information in the distribution of the spatial correlations of entangled twin beams by taking advantage of their dependence on the angular spectrum of the pump needed for four-wave mixing. We show that the encoded information can only be extracted through joint spatial measurements of the twin beams and not through individual beam measurements and that the temporal quantum correlations are not modified. The ability to engineer the spatial properties of twin beams will enable high-capacity quantum networks and quantum-enhanced spatially resolved sensing and imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗

DOE OSTI2022

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

Background: Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive. Methods: This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low. Results: The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity. Conclusions: A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Signatures of optimal codon usage in metabolic genes inform budding yeast ecology

Reverse ecology is the inference of ecological information from patterns of genomic variation. One rich, heretofore underutilized, source of ecologically relevant genomic information is codon optimality or adaptation. Bias toward codons that match the tRNA pool is robustly associated with high gene expression in diverse organisms, suggesting that codon optimization could be used in a reverse ecology framework to identify highly expressed, ecologically relevant genes. To test this hypothesis, we examined the relationship between optimal codon usage in the classic galactose metabolism ( GAL ) pathway and known ecological niches for 329 species of budding yeasts, a diverse subphylum of fungi. We find that optimal codon usage in the GAL pathway is positively correlated with quantitative growth on galactose, suggesting that GAL codon optimization reflects increased capacity to grow on galactose. Optimal codon usage in the GAL pathway is also positively correlated with human-associated ecological niches in yeasts of the CUG-Ser1 clade and with dairy-associated ecological niches in the family Saccharomycetaceae. For example, optimal codon usage of GAL genes is greater than 85% of all genes in the genome of the major human pathogen Candida albicans (CUG-Ser1 clade) and greater than 75% of genes in the genome of the dairy yeast Kluyveromyces lactis (family Saccharomycetaceae). We further find a correlation between optimization in the GAL actose pathway genes and several genes associated with nutrient sensing and metabolism. This work suggests that codon optimization harbors information about the metabolic ecology of microbial eukaryotes. This information may be particularly useful for studying fungal dark matter—species that have yet to be cultured in the lab or have only been identified by genomic material.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Denudation, solute export, landscape evolution modeling, and geographic information system data for the East River watershed, Colorado, USA (2020-2024)

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of lithologic controls on denudation, solute export, carbon-scaling relationships, and transient landscape evolution in the East River watershed near Crested Butte, Colorado, USA. The package includes GIS layers used to produce the Figure 2 map, including drainage, hillshade, lithology, sample locations, and basin polygons, together with comma-separated value (CSV) tables and matching CSV data dictionaries. One group of tables reports sample-level and catchment-level information for river-sediment samples analyzed for in situ-produced cosmogenic beryllium-10 (10Be), including sample names, outlet elevations, geographic coordinates, upstream drainage area, rock-type classes, production-rate scaling scheme, analyzed nuclide, catchment-averaged denudation rates, and associated lower and upper analytical uncertainties. Sample and catchment attributes provide the basis for comparing denudation rates across intrusive, shale, sedimentary, and mixed-lithology settings. A second group of tables reports supporting information for landscape-evolution modeling and the mapped geologic framework of the study area. Included files list parameter values and definitions for the two-phase landscape-evolution simulations, summarize full-domain model erosion fluxes and topographic metrics for different simulation configurations, provide a fixed-area carbon-model scaling table, and summarize mapped geologic units within the East River study domain, including geologic code, formation name, lithologic description, mapped area, and lithologic class grouping. Model outputs and geologic summaries support interpretation of transient landscape behavior and its relation to the mapped distribution of shale, intrusive, sedimentary, and surficial units. A third group of tables reports hydrologic and hydrochemical information used to quantify dissolved export from the watershed. Included files provide site-level values for drainage area, mean annual solute export, standard error of annual export, area-normalized solute yield, and equivalent weathering rate for five East River monitoring sites, along with metadata describing the number, sampling cadence, and date range of discharge records and partial and full total dissolved solids observations used in the solute-yield analyses. The package also contains a supplementary daily ion-load time series with daily mean discharge, discharge observation counts, dissolved concentrations, and daily loads for calcium, magnesium, sodium, potassium, chloride, sulfate, nitrate, fluoride, dissolved silica, charge-balance bicarbonate, and total dissolved solids. The package contains GIS files, comma-separated value files (.csv), CSV data dictionaries, a file-level metadata table, a package-tree text file, and a readme text file.

10Be↗

DOE OSTI2021

Background Information on the Power Quality Requirements in IEEE Std 1547-2018

The revised Institute of Electrical and Electronics Engineers (IEEE) 1547-2018, Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces, was published in April 2018. This standard is one of the foundational documents in the United States needed for integrating distributed energy resources (DERs), including solar energy systems, and energy storage systems with the electric distribution grid. The revised standard contains 11 chapters (clauses) and 8 annexes that comprise 136 pages. The revision is significantly different from the 2003 version, and it contains new concepts and new technical requirements. Each clause specifies information or requirements that apply to certain aspects that are important to the interconnection of DERs to the electric power system. Implementing the requirements necessitates a careful study of the underlying technical concepts and requires appropriate information to calculate relevant settings and configurations. This document provides informative material on the requirements related to electrical power quality in IEEE Std 1547-2018, with the intent to equip the reader with basic knowledge and background information to improve understanding and use of the requirements specified.

14 SOLAR ENERGY↗

DOE OSTI2022

Ontologies: The Gateway to Knowledge-Enabled Information Services at Los Alamos National Laboratory

At LANL, we use ontologies to capture and maintain essential organizational knowledge and support tools and frameworks for information discovery. Ontologies capture knowledge, creating meaningful structures for finding and interpreting information. Information in a human context that provides meaning and data that is organized and communicated. This report provides information about LANL's ontology efforts and challenges.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Resumen del Informe de Progreso del PR100: Resultados Preliminares del Modelo y Conjuntos de Datos Solares y Eólica de Alta Resolución

El estudio de Resiliencia y Transiciones a 100% Energia Renovable de Puerto Rico (PR100) es un estudio de 2 anos de la Oficina de Movilizacion de la Red del Departamento de Energia y seis laboratorios nacionales para analizar exhaustivamente las rutas dirigidas por las personas interesadas hacia un futuro de energia limpia en Puerto Rico. En el Ano 1 del estudio, el equipo PR100, creo y analizo los modelos que alcanzan las metas de energia renovable para Puerto Rico y los objetivos de resiliencia energetica a corto y largo plazo. Este informe, que resume el progreso en el Ano 1, proporciona las consideraciones que pueden informar posibles decisiones de fondos e implementacion potenciales por parte de las agencias federales y locales clave y partes interesadas. El resumen de este informe sigue a la publicacion en julio 2022 de un Informe de Seis Meses de Progreso de PR100. (en ingles y espanol), asi como webinarios publicos en febrero 2022 para lanzar el estudio y julio 2022 para presentar la actualizacion a 6 meses. Un informe final por escrito y visuales por la web seran publicados a finales del 2023. Todas las publicaciones y eventos publicos asociados con el estudio estaran disponibles en ingles y espanol. This report is also available in English https://www.nrel.gov/docs/fy23osti/85018.pdf.

14 SOLAR ENERGY↗

DOE OSTI2023

The Value of Sharing and Consolidating Critical Community, Electricity, and Natural Hazard Information

The California Public Utilities Commission (CPUC) sought support for the development of database schema specifications for a bi-directional data portal that would foster cross-jurisdictional collaboration necessary to improve resilient energy infrastructure planning processes. This technical assistance activity involved Berkeley Lab researchers reviewing 34 local/tribal government hazard mitigation plans to assess (1) the natural hazards that communities are most concerned about; (2) the variety of–and terminology used to describe–critical community infrastructure; and (3) the availability of GIS information that could be incorporated into CPUC-mandated "Microgrid Planning Portals". In addition, we develop a common, but generic data taxonomy showing what fields to collect to encourage consolidating and sharing of this information in the future. Finally, we partnered with the Bishop Paiute tribal government to demonstrate the value of combining electricity infrastructure, natural hazard layers, and critical community infrastructure into a series of maps. The project demonstrated that a single system containing both electric utility and community infrastructure data—as well as information about natural hazards—will help the state of California, the IOUs, first responders, and long-term planners better prepare for—and thus lower their exposure to these ongoing and emerging hazards. The project also identified a number of challenges that will need to be overcome before it is possible to stand up a single system to display this information.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Maximized Information Gain of Next Generation Pulsed Power Using Optimized Design of Z-Machine Experiments

This project develops a Bayesian optimization approach to extracting insights from Z Machine experimental data to determine if and how these insights can be used to extrapolate to a larger facility. The primary goal is to address the scientific challenge of informing how confidently experimental conditions can be predicted on a next generation facility, the design of which requires the reliable extrapolation of current high energy density technologies to regimes yet unobserved, except by costly high-fidelity computational models. Maximizing the use of presently available data and understanding how it informs future endeavors is critically important to enable transformative pulsed power and the science of extreme conditions. We explore a Bayesian optimization approach to experimental design which combines information theory, experimental data, and computational modeling to explore how information gain can be maximized.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Quantum Information Encoding and Decoding for Quantum Sensi

This two-year theory project focused on theoretical investigations of novel paradigms for quantum sensing, building on information encoding and techniques from quantum error correction, quantum computing and other quantum information domains. The outcomes facilitate quantum information technology development, especially at the interface of quantum computing and quantum sensing. The results of the project show new use cases and new paradigms for quantum sensing beyond what has so far been considered. One outcome shows how quantum sensing opens new opportunities for fundamental physics such as the capability of single graviton detection. Another outcome reveals a new application of NISQ quantum computers with error correction for metrology, building on recent advances in practical quantum error correction implementation. The third outcome of the project creates new paradigms of back-action-evading sensing inspired by collective quantum information encoding, which achieves quantum sensing beyond the quantum limit without the use of entanglement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Fossil Corals With Various Degrees of Preservation Can Retain Information About Biomineralization-Related Organic Material

Scleractinian corals typically form a robust calcium carbonate skeleton beneath their living tissue. This skeleton, through its trace element composition and isotope ratios, may record environmental conditions of water surrounding the coral animal. While bulk unrecrystallized aragonite coral skeletons can be used to reconstruct past ocean conditions, corals that have undergone significant diagenesis have altered geochemical signatures and are typically assumed to retain insufficient meaningful information for bulk or macrostructural analysis. However, partially recrystallized skeletons may retain organic molecular components of the skeletal organic matrix (SOM), which is secreted by the animal and directs aspects of the biomineralization process. Some SOM proteins can be retained in fossil corals and can potentially provide past oceanographic, ecological, and indirect genetic information. Here, we describe a dataset of scleractinian coral skeletons, aged from modern to Cretaceous plus a Carboniferous rugosan, characterized for their crystallography, trace element composition, and amino acid compositions. We show that some specimens that are partially recrystallized to calcite yield potentially useful biochemical information whereas complete recrystalization or silicification leads to significant alteration or loss of the SOM fraction. Our analysis is informative to biochemical-paleoceanographers as it suggests that previously discounted partially recrystallized coral skeletons may indeed still be useful at the microstructural level.

58 GEOSCIENCES↗

DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Detecting flash drought events to inform adaptive water management

Abstract: Flash droughts are characterized by a rapid onset of drought conditions, a common feature among various indicators of these events. The lack of a standardized definition and indicator makes flash droughts particularly challenging to predict and manage. We test six state-of-the-art flash drought indicators using 41 years of daily data across all Hydrological Unit Code 4 (HUC4) regions in the contiguous United States. Our results show substantial disagreements among indicators, suggesting that the detection of a flash drought event strongly depends on the choice of indicator. From the perspective of informing management and operations, an open challenge remains: which flash drought indicator should be used to trigger operational adaptations? To this end, we propose a methodological framework that informs adaptive actions by regionally assessing the level of agreement between different indicators. The regional differences in hydroclimatic conditions are expected to be reflected in the level of agreement between indicators. To better inform adaptive actions, the framework will also consider the impacted sectoral water uses and their varying response times. The insights gained from this study improve the understanding of how these different flash drought indicators capture the different aspects of regional differences and impacted sectors. Furthermore, this information can support more targeted adaptive actions to mitigate adverse impacts through improved preparedness and response strategies.

climate resilience↗

NASA NTRS1972

Improved navigation by combining VOR/DME information with air or inertial data

The improvement was determined in navigational accuracy obtainable by combining VOR/DME information (from one or two stations) with air data (airspeed and heading) or with data from an inertial navigation system (INS) by means of a maximum-likelihood filter. It was found that the addition of air data to the information from one VOR/DME station reduces the RMS position error by a factor of about 2, whereas the addition of inertial data from a low-quality INS reduces the RMS position error by a factor of about 3. The use of information from two VOR/DME stations with air or inertial data yields large factors of improvement in RMS position accuracy over the use of a single VOR/DME station, roughly 15 to 20 for the air-data case and 25 to 35 for the inertial-data case. As far as position accuracy is concerned, at most one VOR station need be used. When continuously updating an INS with VOR/DME information, the use of a high-quality INS (0.01 deg/hr gyro drift) instead of a low-quality INS (1.0 deg/hr gyro drift) does not substantially improve position accuracy.

Bobick, J. C.↗

NASA NTRS1971

Information content of simulated space photographs as a function of various levels of image resolution.

The results of research are reported on the information content in simulated space photographs derivable as a function of various levels of image resolution. Whenever certain resource features could not be consistently identified on simulated low-resolution imagery, attempts were made to define the required level of image resolution that would allow a skilled interpreter to discriminate one feature from another. The results of this research, conducted within a chaparral-hardwood-grassland environment in California, indicate that simulated ERTS data contain sufficient information to allow an interpreter to discriminate between woody vegetation, grassland, and water bodies. However, if more detailed information is desired, the imagery must have a ground resolution of at least 50 ft, showing shape, size, texture, and shadow characteristics within each vegetation type. Spaceborne data, therefore, often will have to be supplemented by higher resolution aircraft imagery, depending on the types of resource information being sought.

Lauer, D. T.↗