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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. Further, the performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network

Abstract Mercury's small magnetosphere is embedded in the dynamic and intense solar wind environment characteristic of the inner heliosphere. Both the magnitude and orientation of the interplanetary magnetic field (IMF) significantly influence the solar wind‐magnetospheric interaction at Mercury, driving phenomena such as magnetic reconnection. The MErcury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) spacecraft provided in‐situ magnetic field measurements of the solar wind, the magnetosheath, and the magnetosphere along each orbit. However, it is a challenge to directly assess the IMF's impact on Mercury's plasma environment due to the temporal separation between observations within the solar wind and the magnetosphere, especially in the absence of an upstream monitor. Here, we present a feedforward neural network (FNN) trained on a subset of magnetosheath observations to estimate the strength and orientation of the IMF upstream of the bow shock. Utilizing magnetosheath magnetic field, cylindrical spatial coordinates, and heliocentric distance measurements, the FNN predicts upstream IMF conditions with an score of 0.70 and mean averaged error of 5.3 nT, thereby greatly decreasing the temporal separation between IMF estimates and magnetospheric measurements throughout the MESSENGER mission. This approach yields IMF estimates for all magnetosheath data measured by MESSENGER, providing a useful tool for future investigations of the IMF impact on Mercury's magnetosphere. This method will be integrable with the dual‐spacecraft BepiColombo magnetosheath measurements, providing useful estimates of upstream IMF conditions particularly during the extended periods in which neither spacecraft sample the solar wind. Our results demonstrate the utility of machine learning techniques on advancing space science research.

Bowers, Charles F.↗
DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗
DOE OSTI2025

DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Materials science↗
DOE OSTI2021

Enabling deeper learning on big data for materials informatics applications

Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

UOWDetection

The code provides machine learning, data collection, and computer science tools used to detect undocumented orphaned wells in United States from aerial imagery.

Kim, Anastasiia↗
DOE OSTI2025

DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysis

SF-25-088 Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.

Zhou, Tao [Argonne National Laboratory (ANL), Argo↗
DOE OSTI2021

Integrated parameter and process learning for hydrologic and biogeochemical modules in Earth System Models

Focus area: Primary focal area #2; secondary focal area #3: Learning about parameters and processes of land surface hydrologic and biogeochemical models in Earth System models by integrating machine learning, physics, and big data. Science challenges: How do we maximally leverage big-data observations to improve hydrobiogeochemical process description and parameterization so that such modules more realistically capture hydrologic and vegetation responses and feedbacks under the future climate? For example, how can we leverage physics, limited observations of vegetation and streamflow to better estimate evapotranspiration, and, relatedly, net primary productivity, especially for drought areas? Vegetation plays a critical role in regional and global water cycles; however, existing vegetation models have failed to predict vegetation response to droughts (McDowell & Xu, 2017) , arctic greening (Keenan & Riley, 2018) , and critical transitions between forest and savanna (Hirota et al., 2011) . These studies suggest that when we build process-based models (PBM) parameterized from regional and global plant traits, we tend to poorly describe plant adaptation and local-scale competition processes. The models and their associated parameters assigned for different regions in the world are not capturing essential heterogeneity in vegetation responses at finer spatial scales. Many parameters of the land surface models control hydrology and vegetation dynamics at the same time. The heterogeneity in vegetation response is a function of (i) plant type, (ii) plant size, (iii) competition and succession, (iv) environmental controls, and (v) local variations due to the unique ecological community that are very difficult to describe (e.g., the size of gaps resulting from fire that facilitated the coexistence of pioneering species). In the demographic models, only factors (i) and (iv) were captured, and plant types were generally described only by leaf phenology and climate zones. With current demographic models, we generally consider more traits to define plant types (i) and calibrate these traits to consider factors (ii), (iii) and (iv); however, it is substantially challenging to scale to regional and global simulations due to trait variations across space (Ali et al., 2016). Moreover, it has been noted that hillslope processes, including ridge-to-valley flow and sunny vs. shady slopes are primary organizers of water, energy, and vegetation (Clark et al., 2015; Fan et al., 2019) . Although gradual improvements in the hydrologic model component in earth system models may reduce this error (at a remarkably slow pace), the long-term, gradual impact of hydrology on plant traits are not well captured. Recent work showed that the hydrologic controls exerted by groundwater and lateral flow are primary regulators of rooting depth (Fan et al., 2017) . Such hydrologic controls have seldom been reflected in vegetation model parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cyber-Attack Detection and Accommodation for the Energy Delivery System

The goals of this project were to create a software system with a suite of key algorithms for cyber-attack detection and accommodation providing domain layer protection for critical power generation assets. Example assets included gas and steam turbines, heat recovery steam generators, and electrical generators. The aggressive algorithm goals were aimed at reducing the false positive rates in threat detection to <1% using learnings from many evolving disciplines (power turbine and generator physics, power system modeling, modern control theory, system identification, machine learning, deep learning, mathematics and data science). Additional goals for the algorithms involved localizing threats on-the-fly to know in which monitoring node the effects of attacks are present, and then providing accommodation to keep the system running uninterrupted much of the time in the presence of the attack. Accommodation had a performance goal of providing resiliency when up to 50% of monitoring nodes are in an attack state.

cybersecurity, cyber-physical↗
DOE OSTI2023

BOXKIT

SF-23-067 BoxKit is a library that provides building blocks to parallelize and scale data science, high performance computing, and machine learning applications for block-structured datasets. Spatial data from simulations and experiments can be accessed and managed using tools available in this library when working with more data analysis oriented packages like SciKit (https://github.com/scikit-learn/scikit-learn) and FlowNet (https://github.com/NVIDIA/flownet2-pytorch)

DHRUV, AKASH↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Characterizing Seasonal Variation of the Atmospheric Mixing Layer Height Using Machine Learning Approaches

As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. To examine how these factors vary across seasons, a seasonal analysis is necessary. However, dividing data by season reduces the sample size, which can affect result reliability and complicate factor comparisons. To address these challenges, this study replaces default parameters with grid search optimization and incorporates cross-validation to mitigate dataset limitations. Using XGBoost with four years of data from the atmospheric radiation measurement (ARM) (Southern Great Plains (SGP) C1 site, cross-validation stabilizes correlation coefficient fluctuations from 0.3 to within 0.1. With optimized parameters, the R value can reach 0.81. Analysis of the C1 site reveals that the relative importance of different factors changes across seasons. Lower tropospheric stability (LTS, ~0.53) is the dominant factor at C1 throughout the year. However, during DJF, latent heat flux (LHF, 0.44) surpasses LTS (0.22). In SON, LTS (0.58) becomes more influential than LHF (0.18). Further comparisons among the four long-term SGP sites (C1, E32, E37, and E39) show seasonal variations in relative importance. Notably, during JJA, the differences in the relative importance of the three factors across all sites are lower than in other seasons. This suggests that boundary layer development in the summer is not dominated by a single factor, reflecting a more intricate process likely influenced by seasonal conditions such as enhanced convective activity, higher temperatures, and humidity, which collectively contribute to a balanced distribution of parameter impacts. Furthermore, the relative importance of LTS gradually increases from morning to noon, indicating that LTS becomes more significant as the boundary layer approaches its maximum height. Consequently, the LTS in the early morning in autumn exhibits greater relative importance compared to other seasons. This reflects a faster development of the mixing layer height (MLH) in autumn, suggesting that it is easier to retrieve the MLH from the previous day during this period. The findings enhance understanding of boundary layer evolution and contribute to improved boundary layer parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Can We Use Machine Learning to Control Nuclear Power Plants?

Slides for the INL AI/ML Symposium 11.0 focused on AI/ML in instrumentation, control, and automation. This presentation was on how we can use AI/ML for control of nuclear power plants. A recording of the presentation and other INL AI/ML symposiums can be found at https://inl.gov/artificial-intelligence/.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗
DOE OSTI2022

Technical Performance and Cost Optimization of Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) for Wide-area Surveillance and Identification of Marine Life at Marine Energy Installations

Florida Atlantic University developed an underwater optical monitoring system prototype - Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) - suitable for marine energy full project lifecycle observation (baseline, commissioning, and decommissioning), with an automated real-time classification of marine animals. With precursor 2014 DOE funding (award DE-EE0006787), a prototype UMSLI was demonstrated in a controlled laboratory environment and achieved a TRL 6. This EERE DE-EE0007828 award aimed to both increase the TRL of the UMSLI by improving the technology performance (e.g., increase distance of marine animal target detection capability, add additional species classification capabilities, improve the system performance during the day, etc.) and reduce the cost. The UMSLI presents a novel application of underwater distributed Light Detection And Ranging (LiDAR), an advanced remote sensing method that uses light in the form of laser pulses for an application, this paired with an algorithm provides 360 degrees underwater detection, imaging, and classification of marine life. This solution for underwater monitoring of biota preserves the advantages of traditional optical and acoustic solutions while overcoming many associated disadvantages for marine energy site environmental monitoring, such as difficulties in species detection and classification in low light or night and turbid environments. This new approach is a purposefully designed, reconfigurable adaptation of an existing class 3B laser technology into one UMSLI instrument that can be easily mounted on or around different classes of marine energy equipment, such as devices to capture ocean current energy or wave energy. The system uses relatively low average power, utilizes far-red (> 635nm) laser illumination to be invisible and eye-safe to marine animals, is compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations (i.e., current design is targeting more than 7 days continuous operation), to inherently generate a sparse primary dataset that only includes detected anomalies, and to allow robust real-time automated animal classification and identification with a low data bandwidth requirement. The technology’s overarching goal for application, is a system that can be deployed to collect pre-installation baseline species observations at a proposed marine energy deployment site with minimal post-processing overhead. The envisioned system will also produce high-resolution imagery of marine animals through a wide range of conditions and support automated tracking and notification of the presence of managed animals within established perimeters of marine energy equipment to satisfy deployed marine energy projects’ endangered and threatened species monitoring requirements. Through the current project, we demonstrated the UMSLI prototype in an operational environment and increased the UMSLI’s Technology Readiness Level from 6 to 7. The project resulted in many novel technologies, including: 1) an eye-safe red laser based, low-cost LiDAR system that can detect targets up to 10 meters distance; 2) the GAN-based machine learning underwater LiDAR image enhancement technique (this is the first known application of GAN technique in underwater LiDAR); 3) LiDAR-based real-time automated detection capabilities; and 4) a template matching based automated classification tool. These technologies build a solid foundation for future efforts to develop an extended range electro-optical monitoring system suitable for marine energy deployments. In addition to technology development, a key lesson learned is that addressing regulatory and safety requirements must be front and center in any marine energy monitoring applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Learning Coagulation Processes With Combinatorial Neural Networks

Abstract Simulating the evolution of a coagulating aerosol or cloud of droplets in a key problem in atmospheric science. We present a proof of concept for modeling coagulation processes using a novel combinatorial neural network (CombNN) architecture. Using two types of data from a high‐detail particle‐resolved aerosol simulation, we show that CombNN models outperform standard neural networks and are competitive in accuracy with traditional state‐of‐the‐art sectional models. These CombNN models could have application in learning coarse‐grained coagulation models for multi‐species aerosols and for learning coagulation models from observed size‐distribution data.

54 ENVIRONMENTAL SCIENCES↗