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DOE OSTI2024

Data-driven projection pursuit adaptation of polynomial chaos expansions for dependent high-dimensional parameters

Uncertainty quantification (UQ) and inference involving a large number of parameters are valuable tools for problems associated with heterogeneous and non-stationary behaviors. The difficulty with these problems is exacerbated when these parameters are statistically dependent requiring statistical characterization over joint measures. Probabilistic modeling methodologies stand as effective tools in the realms of UQ and inference. Among these, polynomial chaos expansions (PCE), when adapted to low-dimensional quantities of interest (QoI), provide effective yet accurate approximations for these QoI in terms of an adapted orthogonal basis. These adaptation techniques have been cast as projection pursuits in Gaussian Hilbert space in what has been referred to as a projection pursuit adaptation (PPA) by Xiaoshu Zeng and Roger Ghanem (2023). The PPA method efficiently identifies an optimal low-dimensional space for representing the QoI and simultaneously evaluates an optimal PCE within that space. The quality of this approximation clearly depends on the size of the training dataset, which is typically a function of the adapted reduced dimension. Here, the complexity of the problem is thus mediated by the complexity of the low-dimensional quantity of interest and not the complexity of the high-dimensional parameter space.

Data-driven↗

DOE OSTI2021

Avoiding biases in binned fits

Abstract Binned maximum likelihood fits are an attractive option when analysing large datasets, but require care when computing likelihoods of continuous PDFs in bins.For many years the widely used statistical modelling package evaluated probabilities at the bin centre, leading to significant biases for strongly curved probability density functions.We demonstrate the biases with real-world examples, and introduce a PDF class to that removes these biases.The physics and computation performance of this new class are discussed.

Instruments & Instrumentation↗

DOE OSTI2024

ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applications

Cybersecurity breaches targeting electrical substations constitute a significant threat to the integrity of the power grid, necessitating comprehensive defense and mitigation strategies. Any anomaly in information and communication technology (ICT) should be detected for secure communications between devices in digital substations. This paper proposes large language models (LLMs), e.g., ChatGPT, for the cybersecurity of IEC 61850-based communications. Multi-cast messages such as generic object oriented system events (GOOSE) and sampled values (SV) are used for case studies. The proposed LLM-based cybersecurity framework includes, for the first time, data pre-processing of communication systems and human-in-the-loop (HITL) training (considering the cybersecurity guidelines recommended by humans). The results show a comparative analysis of detected anomaly data carried out based on the performance evaluation metrics for different LLMs. A hardware-in-the-loop (HIL) testbed is used to generate and extract a dataset of IEC 61850 communications.

ChatGPT↗

DOE OSTI2026

Fleet Utilization

A key goal of NextGen Profiles' fleet utilization study was to conduct a comprehensive, strategic, and standardized assessment of the operational behavior and utilization patterns across EV and EVSE production-ready fleets. These data-driven insights were intended to inform current fleet management strategies and support future infrastructure planning, ensuring the effective adoption and adaptation of the growing EV fleet market. The study applied a series of metrics defined in NextGen Profiles to evaluate diverse fleet operations across various use cases, emphasizing trends in charging, routing, and other critical behaviors. The fleet utilization dataset includes these three sets of metrics from 17 EV fleets, each consisting of a wide range of vehicle types and operational categories, as well as two EVSE fleets. Data were collected from a variety of sources and reformatted into a unified structure before metric computation, ensuring consistency and comparability across all fleets. To protect confidentiality, all fleet metadata are anonymized, and the publicly released metric datasets are aggregated to an hourly cadence.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Evaluating Symmetry in 3-dimensional Simulation Data

The increasing use of three-dimensional simulations has led to an increased need for effective verification and validation, or V&V. Unfortunately, the high computational cost associated with three-dimensional simulations makes many standard V&V techniques cost-prohibitive. Given these circumstances, we must pay close attention to V&V metrics that can be evaluated using only a single simulation. One of the simplest of these metrics is the evaluation of physical symmetries. This report details a method of computing the deviation from symmetry in a three-dimensional dataset with an arbitrarily distributed mesh. Examples are presented for spherical and axisymmetric systems using the Paraview post-processing analysis workflow.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗

DOE OSTI2026

The Role of the U.S. Electric Distribution System in Serving Data Center and Other Large Loads

The rapid expansion of data centers in the United States is reshaping how the electric distribution system must plan for and accommodate large load interconnections. This report evaluates the role of the distribution grid in serving these loads, from small edge facilities to hyperscale campuses. Using national datasets, utility filings, and industry studies, we assess demand growth, reliability requirements, interconnection thresholds, and infrastructure needs at substations and feeders. The analysis highlights the mismatch between fast data center development timelines and slower utility planning and construction cycles, as well as strategies such as phased energization, on-site generation, hosting capacity maps, and structured interconnection frameworks. While focused on data centers, the insights also apply to other high-density loads such as advanced manufacturing, hydrogen production, and electrified transportation. The report concludes with approaches to align planning processes, transparency tools, and regulatory frameworks so utilities can manage new large loads in ways that support a reliable and resilient grid.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin - Task 2 Topical Report

This attachment A is a detailed Topical Report for Task 2 (Advanced Field Characterization and Machine Learning Based Data Integration) under the project "Chemically Enabled CO 2 -Enhanced Oil Recovery in Multi-Porosity, Hydrothermally Altered Carbonates in the Southern Michigan Basin." The overall project activity and finding, including the field pilot testing of CO 2 injection are summarized in the companion Final Technical Report The Advanced Field Characterization and Machine Learning Based Data Integration task (Task 2) involved a systematic geologic characterization of the Trenton-Black River (TBR) play in the SMB which included the development of comprehensive datasets, advanced data analytics, risk assessment, and piggyback field characterization. These activities aimed to address the complex carbonate systems by evaluating the extent of fractures, dolomitization, facies, and reservoir properties with the primary objective of informing the static and dynamic modeling, field injection test, and providing input into the development strategy plan. The task was divided into four subtasks: • Subtask 2.1 – Data compilation, review, and analysis • Subtask 2.2 – Risk Assessment • Subtask 2.3 – Advanced Field Characterization • Subtask 2.4 – Integrated Physics-Based Machine Learning and Advanced Data Analytics

02 PETROLEUM↗

DOE OSTI2026

Observed Atmospheric River Events

This dataset contains observed landfalling atmospheric river (AR) events over the Northeast Pacific and U.S. West Coast used to initialize and evaluate the Deep-AR synthetic atmospheric river framework. It includes 1,434 held-out events whose observed records begin 48 hours before diagnosed AR landfall and continue at 6-hour intervals over a 144-hour event window. Each HDF5 file provides high-resolution 0.25° gridded fields of integrated vapor transport components (qu, qv), 10 m wind components (u10, v10), and 6-hour accumulated precipitation. IVT components and precipitation are sourced from EDARA; 10 m winds are sourced from ERA5. The records share a common 200 × 480 grid over the Northeast Pacific and U.S. West Coast and include coordinate and datetime arrays. These events support atmospheric-river hazard analysis, evaluation of synthetic event generation, precipitation-extremes research, and regional stress testing.

17 WIND ENERGY↗

DOE OSTI2026

Improved method for temporally interpolating radiosonde profiles in the convective boundary layer

A significantly improved technique for temporally interpolating radiosonde (RS) profiles of potential temperature and water vapor mixing ratio in the planetary boundary layer during daytime is introduced. The key innovation of this technique is its operation on a height grid normalized with the planetary boundary layer height. This study utilized a three-month dataset of three-hourly soundings from the Atmospheric Radiation Measurement Facility's Southern Great Plains site. The technique was evaluated for convective boundary layer cases, with the necessary boundary layer height data obtained from a ground-based infrared spectrometer. A total of 79 comparisons were conducted between reference soundings and interpolated profiles that did and did not employ height normalization. The results demonstrated a substantial improvement in the representation of interpolated profiles using the new technique, characterized by enhanced correlation, improved amplitude representation, and reduced bias for potential temperature, as well as improved correlation and reduced bias for water vapor mixing ratio.

convective boundary layer↗

NASA NTRS2004

Radiation Risk Assessment of the Individual Astronaut: A Complement to Radiation Interests at the NIH

Predicting human risks following exposure to space radiation is uncertain in part because of unpredictable distribution of high-LET and low-dose-derived damage amongst cells in tissues, unknown synergistic effects of microgravity upon gene- and protein-expression, and inadequately modeled processing of radiation-induced damage within cells to produce rare and late-appearing malignant cancers. Furthermore, estimation of risks of radiogenic outcome within small numbers of astronauts is not possible using classic epidemiologic study. It therefore seems useful to develop strategies of risk-assessment based upon large datasets acquired from correlated biological models useful for resolving radiogenic risk-assessment for irradiated individuals. In this regard, it is suggested that sensitive cellular biodosimeters that simultaneously report 1) the quantity of absorbed dose after exposure to ionizing radiation, 2) the quality of radiation delivering that dose, and 3) the biomolecular risk of malignant transformation be developed in order to resolve these NASA-specific challenges. Multiparametric cellular biodosimeters could be developed using analyses of gene-expression and protein-expression whereby large datasets of cellular response to radiation-induced damage are analyzed for markers predictive for acute response as well as cancer-risk. A new paradigm is accordingly addressed wherein genomic and proteomic datasets are registered and interrogated in order to provide statistically significant dose-dependent risk estimation in individual astronauts. This evaluation of the individual for assessment of radiogenic outcomes connects to NIH program in that such a paradigm also supports assignment of a given patient to a specific therapy, the diagnosis of response of that patient to therapy, and the prediction of risks accumulated by that patient during therapy - such as risks incurred by scatter and neutrons produced during high-energy Intensity-Modulated Radiation Therapy. Value of assessment of radiogenic outcome for individuals exposed to radiation is suggested to be common to both NASA and NIH.

Richmond, Robert C.↗

NASA NTRS2012

Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

Clouds cover about 70% of the Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the whole globe and across the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than 25 years in length. However, climatologies compiled from different satellite datasets can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel, provided the first coordinated intercomparison of publically available, standard global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (some with multiangle view and polarization capabilities), IR sounders and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature or altitude), cloud radiative properties (optical depth or emissivity), cloud thermodynamic phase and bulk microphysical properties (effective particle size and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. A monthly, gridded database, in common format, facilitates further assessments, climate studies and the evaluation of climate models.

Stubenrauch, C. J.↗

NASA NTRS2013

Hadoop for High-Performance Climate Analytics: Use Cases and Lessons Learned

Scientific data services are a critical aspect of the NASA Center for Climate Simulations mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyber infrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model inter-comparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.

analytics↗

NASA NTRS2015

A New Lunar Digital Elevation Model from the Lunar Orbiter Laser Altimeter and SELENE Terrain Camera

We present an improved lunar digital elevation model (DEM) covering latitudes within +/-60 deg, at a horizontal resolution of 512 pixels per degree ( approx.60 m at the equator) and a typical vertical accuracy approx.3 to 4 m. This DEM is constructed from approx.4.5 ×10(exp 9) geodetically-accurate topographic heights from the Lunar Orbiter Laser Altimeter (LOLA) onboard the Lunar Reconnaissance Orbiter, to which we co-registered 43,200 stereo-derived DEMs (each 1 deg×1 deg) from the SELENE Terrain Camera (TC) ( approx.10(exp 10) pixels total). After co-registration, approximately 90% of the TC DEMs show root-mean-square vertical residuals with the LOLA data of < 5 m compared to approx.50% prior to co-registration. We use the co-registered TC data to estimate and correct orbital and pointing geolocation errors from the LOLA altimetric profiles (typically amounting to < 10 m horizontally and < 1 m vertically). By combining both co-registered datasets, we obtain a near-global DEM with high geodetic accuracy, and without the need for surface interpolation. We evaluate the resulting LOLA + TC merged DEM (designated as "SLDEM2015") with particular attention to quantifying seams and crossover errors.

Barker, M. K.↗

NASA NTRS2023

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid-Latitude Stations

The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

Michael Coughlan↗

NASA NTRS2023

Commercial Smallsat Data Acquisition Program: Airbus U.S. Synthetic Aperture Radar Quality Assessment Summary

Quality assessment of the Airbus X-band Synthetic Aperture Radar (SAR) satellite products was conducted by the Commercial Smallsat Data Acquisition (CSDA) program’s radar subject matter experts, following the Joint NASA/ESA (European Space Agency) assessment draft guidelines. All three Airbus SAR spacecraft (TerraSAR-X, TanDEM-X, and PAZ) are based on the TerraSAR-X platform, and each have an active phased array antenna that is 4.8 x 0.7 m in the along-track and cross-track dimensions, respectively. TerraSAR-X and TanDEM-X are in a helical orbit, creating a bistatic imaging geometry, in addition to being capable of independent monostatic observations. The PAZ mission follows TerraSAR-X and TanDEM-X in the same 11-day orbit with a 5.5-day lag. TerraSAR-X and TanDEM-X are designed, developed, and operated through a Public-Private Partnership, while PAZ is a dual-use mission (civil and defense agencies), funded and owned by the Spanish Ministry of Defense and managed by Hisdesat (Hisdesat Servicios Estratégicos, S.A.), a Spanish private communications company. The assessment presented in this document is divided into two main parts: documentation review and the assessment of test datasets. The documentation review in sections 2.1 through 2.4 includes the assessment of the Airbus documentation provided to the CSDA evaluation team. The grading of these documents is given in columns 1-4 of the maturity matrix shown in section 1.1. Section 2.5 summarizes the evaluation performed by NASA using the data purchased through the CSDA program. The grading for this is given in the last column of the maturity matrix. Section 3 provides more detailed explanations on the methods and the results of the data analysis performed by NASA. Only the documents provided by Airbus for the evaluation were considered for the review. Additional documentation with more detailed description of the calibration and validation procedures may be available online but were not considered for this evaluation. The product information provided in the available documentation (RD-1, RD-2) and the product metadata together provided adequate information to work with the data. The product details in the metadata included the required information to work with the data in the common XML file format. Metrological traceability documentation was not provided to CSDA. All relevant characterization of the SAR system and data were provided, and the metadata include all relevant ancillary information. Documentation provided to CSDA included limited pre-flight and post-launch calibration information.

Batuhan Osmanoglu↗

DOE OSTI2020

SFCOMPO Database of Spent Nuclear Fuel Assay Data – the Next Frontier

SFCOMPO is the world’s largest database for measured spent nuclear fuel assay data. An international effort coordinated by the Nuclear Energy Agency (NEA) resulted in a significant expansion of the database and its release online in 2017 as a downloadable application. The SFCOMPO Technical Review Group (TRG) was recently formed under the direction of NEA’s Nuclear Science Committee/Working Party on Nuclear Criticality Safety and was mandated to maintain and further coordinate the development of SFCOMPO. This TRG is currently focused on (1) critical evaluation of the experimental assay data by independent experts and (2) development of benchmarks and benchmark models that can be applied to validate burnup codes. This will improve the quality and documentation of the experimental datasets and enable their use by the international community to support code validation for design and safety analysis of spent nuclear fuel transportation, storage, and repository applications. It follows the precedent and draws on the experience gained from similar NEA efforts in the International Reactor Physics Experiment Evaluation Project and the International Criticality Safety Benchmark Experiment Project. Ongoing SFCOMPO evaluations have served as a test bed to develop templates for documenting evaluations, develop review guidance, improve approaches for a global uncertainty analysis, and devise a strategy focused on providing practical information of highest value to the user community. The current effort, status, and associated challenges are discussed.

Ilas, Germina↗