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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An Efficient Storage-Driven Machine Learning Model for Performance in the Era of Multimodal Scientific Data

Scientific workflows are increasingly relying on machine learning (ML), simulation, and hybrid techniques to predict, understand, and optimize the behavior of complex experiments. High-performance computing has greatly improved researchers’ ability to acquire diverse data modalities in these workflows. Recent studies suggest that the performance of machine learning models can be improved by integrating data from various sources. Unfortunately, these workloads pose unprecedent pressure on the network storage to meet the demands associated with accessing these multimodal data. To mitigate the impact of intensive IO, we propose a solution that utilizes a multi-tier High-Performance Computing (HPC) distributed storage and data processing framework, placing computation where the data resides for better performance. By adopting this project, the scientific community will gain new opportunities to explore multimodal storage-driven possibilities, integrating multiple scientific data sources with advanced streaming frameworks. Additionally, our framework effectively utilizes computing resources and bridges the gaps identified by HPC experts. Our proposed approach tackles scalability and persistence challenges by leveraging native persistency, which has posed difficulties in traditional approaches. Furthermore, we seek to enhance fault-tolerance and load-balance of computations by leveraging real-time streaming in diverse scientific computing environments, thereby propelling advanced scientific computing research into the next generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Reducing Operator Complexity of Galerkin Coarse-grid Operators with Machine Learning

Here, we propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in multigrid (MG) methods, addressing the well-known issue of increasing operator complexity. Guided by the MG theory on spectrally equivalent coarse-grid operators, we have developed novel machine learning algorithms that utilize neural networks combined with smooth test vectors from multigrid eigenvalue problems. The proposed method demonstrates promise in reducing the complexity of coarse-grid operators while maintaining overall MG convergence for solving parametric partial differential equation problems. Numerical experiments on anisotropic rotated Laplacian and linear elasticity problems are provided to showcase the performance and comparison with existing methods for computing non-Galerkin coarse-grid operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning tools for epigenetics

The software provides machine learning analysis and visualization to detect patterns in epigenetic data, including conventional machine learning and statistical methods, and open-source packages like pyBigWig (https://github.com/deeptools/pyBigWig) for data processing. The software is written in python, it uses some python libraries.

Kim, Anastasiia↗
DOE OSTI2025

CAML: Commutative Algebra Machine Learning─A Case Study on Protein–Ligand Binding Affinity Prediction

Recently, Suwayyid and Wei introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we propose commutative algebra machine learning (CAML) for the prediction of protein−ligand binding affinities. Specifically, we apply persistent Stanley−Reisner theory, a key concept in combinatorial commutative algebra, to the affinity predictions of protein−ligand binding and metalloprotein−ligand binding. We present three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to tackle the complexity of data involved in (metallo) protein−ligand complexes. We show that the proposed CAML outperforms other state-of-theart methods in (metallo) protein−ligand binding affinity predictions, indicating the great potential of commutative algebra learning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Analysis of Slow Spill Data for Mu2e

The Mu2e experiment requires a constant, relatively low intensity muon beam to produce data with high clarity, which can be achieved using slow extraction. Slow spills/extractions in the Delivery Ring involve contracting and expanding the stable region, which is bordered by the separatrix, of the beam pipe. While this does lower the beam intensity, it is very inconsistent. To help mitigate future inconsistencies, data from many trial spills (some including various magnet impulses to influence the beam intensity) was examined. This involved cutting low quality spills that have abnormal peak and integrated intensities, as well as spills with unusually low magnet ramping. Then, the remaining spills in the datasets were analyzed for trends within spills and across many spills. The findings from this analysis were then given to the FAN-C team to help them develop their simulations, as well as provide training data for their machine learning models that will use beam and impulse data to apply corrective impulses during future slow extractions.

Osborn, Thomas [Purdue U., West Lafayette]↗
DOE OSTI2024

Probabilistic data fusion and physics-informed machine learning: A new paradigm for modeling under uncertainty, and its application to accelerating the discovery of new materials

In this report we summarize the work conducted by PI Perdikaris and his group under this Early Career project DE–SC0019116 during the period of 09/01/2018 – 08/31/2023. The central aim of the work was to introduce a new paradigm for scientific data analysis that can seamlessly synthesize rigorous mathematical modeling with data of variable fidelity (e.g., measurements at multiple scales/resolutions or predictions of variable fidelity models) and multiple modalities (e.g., images, time–series, or scattered measurements). The setting we are interested in involves complex systems that are partially observed and whose dynamical behavior could be hard to model or totally unknown. The inherent uncertainty associated with this setting necessitates a departure from the classical deterministic realm of modeling and scientific computation, and, consequently, our main building blocks can no longer be crisp deterministic numbers and governing laws, but instead we must operate with probabilistic models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California

Abstract After large-magnitude earthquakes, a crucial task for impact assessment is to rapidly and accurately estimate the ground shaking in the affected region. To satisfy real-time constraints, intensity measures are traditionally evaluated with empirical Ground Motion Models that can drastically limit the accuracy of the estimated values. As an alternative, here we present Machine Learning strategies trained on physics-based simulations that require similar evaluation times. We trained and validated the proposed Machine Learning-based Estimator for ground shaking maps with one of the largest existing datasets (<100M simulated seismograms) from CyberShake developed by the Southern California Earthquake Center covering the Los Angeles basin. For a well-tailored synthetic database, our predictions outperform empirical Ground Motion Models provided that the events considered are compatible with the training data. Using the proposed strategy we show significant error reductions not only for synthetic, but also for five real historical earthquakes, relative to empirical Ground Motion Models.

Environmental Sciences & Ecology↗
DOE OSTI2025

SEAFORML (Smart Exploration and Analysis For Optimal and Robust Machine Learning)

The poster discusses data analysis of the WAVgraph database and applied machine learning methods for it. The database is a long-term project that seeks to be a comprehensive repository of information on cyber threats and is updated regularly. It was previously unanalyzed and unexplored. The goal was to learn more about it and its contents in order to have a better understanding and enable better use. The data analysis and discovery enabled further exploration through natural language processing, similarity, and clustering methods. The poster shows some of the insights from the analysis and explains the methods used for the machine learning applications.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A model to assess Zircaloy’s mechanical property changes following a transient beyond critical heat flux

Maintaining the integrity of nuclear fuel rods is essential for ensuring public health and safety in nuclear power generation. During reactor operation, this integrity is confirmed by demonstrating compliance with established regulatory acceptance criteria. For moderate-frequency events, such as limiting transients and anticipated operational occurrences (AOOs), the current fuel integrity criterion is based on preventing boiling transition. This criterion assumes that prevention of boiling transition will prevent excessive cladding heating and, thus, fuel failure during normal operations. While conservative, this approach places significant constraints on core design, fuel cycle economics, and a plant’s ability to perform major power uprates, leading to suboptimal fuel utilization and inefficient carbon-free energy production. A more efficient approach could be achieved by revising the failure criterion to a material-specific limit rather than strictly preventing the boiling transition, since boiling transition per se is not a cause of fuel cladding failure. Here, as a result, a new licensing framework based on material properties, termed time-at-temperature (t@T), is needed. This approach would allow for brief periods of post–critical heat flux operation during an AOO without compromising safety. Implementing the t@T licensing strategy requires a robust technical foundation in material properties, which must be established through comprehensive data collection on both unirradiated and irradiated fuel and cladding materials. This foundation would enable the development of a safety basis that ensures safe operation while providing greater flexibility and efficiency for reactor operation. This paper documents a thorough review of the available data to establish a baseline knowledge that can inform the development of cladding mechanical models, as well as identify experimental data gaps that need to be addressed in future research. Machine learning and data informatics were utilized to extract the importance of parameters on the t@T parameter. Industry tools were used to perform baseline analyses to define the relevant transient conditions for data analysis. The subsequent review successfully identified applicable experimental data, as well as sufficient data to evaluate changes in cladding mechanical properties following an AOO transient. Rather than developing new models, this work coupled existing irradiation annealing and recrystallization models to calculate changes in hardness, yield stress, and ultimate tensile stress following an AOO event. The findings from this review were summarized to highlight the experimental data needs required to fill remaining gaps and support the development of future t@T licensing methodologies.

Cladding performance↗
DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗
DOE OSTI2024

Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing

The Physics-Guided Machine Learning (PGML) for Improved Aerostructure Manufacturing project objective is to automatically tune machining parameter predictions from physics-based models using process data and Bayesian machine learning. The intent is to enable a step change in aerospace manufacturing by combining machine learning, physics-based process models, and sensors/data in a comprehensive digital environment that simultaneously considers the computer numerically controlled (CNC) machining center capabilities, the workpiece material and geometry, and the workpiece support (fixturing). The project hypothesis is that this combination will enable improved performance in machining operations.

42 ENGINEERING↗
DOE OSTI2025

Online Electron Reconstruction at CLAS12

Online reconstruction plays a crucial role in monitoring and in real-time analysis of high energy and nuclear physics experiments. A vital aspect of reconstruction algorithms is particle identification, which combines information from various detector components to determine the type of particle. Electron identification is particularly significant in electro-production nuclear physics experiments like the CLAS12 spectrometer at Jefferson Laboratory as it is essential in data recording. A machine learning approach has been developed for CLAS12 experiments to reconstruct and identify electrons by combining raw signals from multiple detector components at the data acquisition level. This method achieves high electron identification purity while maintaining nearly 100% efficiency. Furthermore, the machine learning tools operate at rates exceeding data acquisition speed, enabling the real-time electron reconstruction. This advancement significantly improves online analyses and monitoring capabilities for CLAS12 experiments.

Tyson,, Richard [Thomas Jefferson National Acceler↗