Search NASA⌕ Search

SEARCH · Search NASA

Results for “Machine Learning Algorithms”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Principal Component and Machine Learning Approach to Spatially Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals tend to be limited in spatial coverage due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals but despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we propose a spatial gap filling approach using machine learning to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from a standard ocean color algorithm such as the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) which will be onboard NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the geostationary satellite Tropospheric Emissions: Monitoring of Pollution (TEMPO) to better understand diurnal variability in ocean ecology.

MODIS atmospheric correction algorithm↗
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 OSTI2025

FY25 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and image 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 cracks 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, the identification of potential cracks was prioritized for the past several years at the request of program leadership.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER?s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use. PowerPoint for conference that was reviewed in PRS and LRS PRS/CON-25-05379 and INL/CON-25-82946

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗
NASA NTRS2021

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗
NASA NTRS2019

Machine Learning, Markov Chain Monte Carlo, and Optimal Algorithms to Characterize the AdvACT Kilopixel Transition-Edge Sensor Arrays

Next-generation focal planes comprising dozens of kilopixel transition-edge sensor (TES) arrays require new methods to rapidly screen candidate arrays, evaluate array non-idealities in the field, identify outlier devices for removal, and optimize the array performance in the field. We demonstrate robust methods to estimate TES parameters (critical temperatures and thermal conductivity parameters) and their uncertainties using a custom Markov Chain Monte Carlo (MCMC) algorithm. We also constrain systematic effects in estimating the TES parameters from non-isothermal current-voltage curves (IVs) at approximately a ~3% level. Additionally, for the first time, we have applied Machine Learning (ML) algorithms to tune detector arrays and optimize their performance.

Maria Salatino↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER’s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS2014

Learning Extended Finite State Machines

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library.

Register Automata↗
NASA NTRS2000

Autonomous Performance Monitoring System: Monitoring and Self-Tuning (MAST)

Maintaining the long-term performance of software onboard a spacecraft can be a major factor in the cost of operations. In particular, the task of controlling and maintaining a future mission of distributed spacecraft will undoubtedly pose a great challenge, since the complexity of multiple spacecraft flying in formation grows rapidly as the number of spacecraft in the formation increases. Eventually, new approaches will be required in developing viable control systems that can handle the complexity of the data and that are flexible, reliable and efficient. In this paper we propose a methodology that aims to maintain the accuracy of flight software, while reducing the computational complexity of software tuning tasks. The proposed Monitoring and Self-Tuning (MAST) method consists of two parts: a flight software monitoring algorithm and a tuning algorithm. The dependency on the software being monitored is mostly contained in the monitoring process, while the tuning process is a generic algorithm independent of the detailed knowledge on the software. This architecture will enable MAST to be applicable to different onboard software controlling various dynamics of the spacecraft, such as attitude self-calibration, and formation control. An advantage of MAST over conventional techniques such as filter or batch least square is that the tuning algorithm uses machine learning approach to handle uncertainty in the problem domain, resulting in reducing over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on cumulative probability generated by the historical performance of the system. The success of MAST will depend heavily on the reinforcement scheme used in the tuning algorithm, which guarantees the tuning solutions exist.

Peterson, Chariya↗
DOE OSTI2024

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

Methane (CH 4 ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH 4 fluxes is examined at 14 FLUXNET- CH 4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH 4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH 4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Algorithmic Classification of Raman Spectra Biosignatures: Improving Life Detection Confidence

“Agnostic” biosignatures – indicators of life (or the absence of life), independent of a particular biochemistry – are increasingly considered a high standard for life detection. The Ladder of Life Detection (2018) called for investigating how combinations of independent and different potential biosignatures affect confidence. To address this gap, statistical classification of elemental abundances, isotopic fractionation, and reflectance spectroscopy (VNIR) has been implemented. Raman spectroscopy, highly desirable due to its wide availability, has the potential to improve this predictive power. This work implemented biosignature classification algorithms on Raman data alone, in preparation for combination with the other data types. Raman spectroscopy data was collected from published databases and papers as part of a manually curated dataset of “indicative” and “non-indicative of life” samples. These currently include 61 non-indicative samples (meteorites, magnetite); 3 indicative living samples (bacteria); 20 indicative non-living samples (chalk, bone); and 12 indicative mixed (with non-indicative material) samples (soil, microbial mats). Laboratory work is ongoing to characterize additional samples, particularly a greater breadth of mixed systems. Spectra were interpolated, filtered with the Savitzsky-Golay filter, and de-noised. For a preliminary examination, agnostic features were manually extracted including mean intensity, number of peaks, and mean peak width. Different peak prominences and filtering polynomials were used to refine features. Classification algorithms were implemented: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), random forest (RF), Gaussian naïve bayes (GNB). Lastly, Monte Carlo simulations on 1,000 50%-train-test-splits were used to validate classification performance and feature significance. The preliminary feature set achieved its highest AUC of 0.52 with LR, with no strongly discriminatory features. Work to improve feature extraction, such as through deep learning with back propagation, is planned. In future work, the Raman data will be combined with the other data types, and potentially new data types such as enantiomeric excess. This project was partially supported through the NASA Ames Project EXcellence (APEX) incubator program.

Astrobiology↗
NASA NTRSDate not supplied

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗
DOE OSTI2024

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO 2 and lignin dissolution

The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π*). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R 2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Autonomous anomaly detection of proliferation in the AGN-201 nuclear reactor digital twin

The expansion of global nuclear power necessitates advanced methods for analyzing proliferation indicators. This study introduces a novel application of the Isolation Forest Machine Learning (IFML) algorithm within a digital twin (DT) of the AGN-201 nuclear reactor to autonomously detect anomalies. Leveraging real-time operational data from the AGN-201 DT, the IFML algorithm identifies outliers without prior data labeling and operates as a lightweight, complementary approach to traditional physics-based anomaly detection methods for nuclear safeguards. In a simulated Red vs. Blue team exercise, the IFML algorithm successfully detected six significant unseen anomalies related to reactivity changes, achieving an accuracy of 99% for identifying operational deviationxs. These anomalies, caused by deliberate perturbations, were detected alongside known physics-based models, underscoring the potential of IFML to enhance real-time monitoring without displacing traditional methods. Further, this study highlights the applicability of IFML in nuclear environments by providing an additional, redundant layer of anomaly detection to improve safeguards and operational safety in complex systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

Here, this study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗