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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Deep learning based x-ray spectrometer for high repetition rate characterization of betatron radiation

Betatron radiation produced from a laser-wakefield accelerator is a broadband, hard x-ray (>1 keV) source that has been used in a variety of applications in medicine, engineering, and fundamental science. Further development and optimization of stable, high repetition rate (HRR) (>1 Hz) betatron sources will provide a means to extend their application base to include single-shot dynamical measurements of ultrafast processes or dense materials. Recent advances in laser technology used in such experiments have enabled increases in shot-rate and system stability, providing improved statistical analysis and detailed parameter scans. However, unique challenges exist at high repetition rate, where data throughput and source optimization are now limited by diagnostic acquisition rates and analysis. Here, we present the development of a machine-learning algorithm for the real-time analysis of betatron radiation. We report on the fielding of this deep learning algorithm for online source characterization at the Institut National de la Recherche Scientifique's Advanced Laser Light Source. By fine-tuning an algorithm originally trained on a fully synthetic dataset using a subset of experimental data, the algorithm can predict the betatron critical energy with a percent error of 7.2 % with a reconstruction time of 1.5 ms, providing a valuable tool for real-time, multi-objective optimization at HRR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

Operations on Graphical Models with Plates

This paper explains how graphical models, for instance Bayesian or Markov networks, can be extended to model problems in data analysis and learning. This provides a unified framework that combines lessons learned from the artificial intelligence, statistical and connectionist communities. This also offers a set of principles for developing a software generator for data analysis, whereby a learning or discovery system can be compiled from specifications. Many of the popular learning algorithms can be compiled in this way from graphical specifications. While in a sense this paper is a multidisciplinary review of learning, the main contribution here is the presentation of the material within the unifying framework of graphical models, and the observation that, as a result, the process of developing learning algorithms can be partly automated.

Buntine, Wray L.↗
NASA NTRS2017

Assessment of Cognitive Communications Interest Areas for NASA Needs and Benefits

This effort provides a survey and assessment of various cognitive communications interest areas, including node-to-node link optimization, intelligent routing/networking, and learning algorithms, and is conducted primarily from the perspective of NASA space communications needs and benefits. Areas of consideration include optimization methods, learning algorithms, and candidate implementations/technologies. Assessments of current research efforts are provided with mention of areas for further investment. Other considerations, such as antenna technologies and cognitive radio platforms, are briefly provided as well.

Knoblock, Eric J.↗
NASA NTRS1997

Convergence Analysis of a Cascade Architecture Neural Network

In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural networks. From this, a mathematical foundation for the casade correlation learning algorithm can also be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case of an efficient hardware learning algorithm called Cascade Error Projection.

Neural Network↗
DOE OSTI2026

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
NASA NTRS2004

Active learning in the presence of unlabelable examples

We propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.

active learning↗
NASA NTRS1972

An estimation algorithm with learning feature for an adaptive bit synchronizer.

Description of an on-line optimal (in the sense of linear minimum mean square error) sequential bit synchronizer assuming minimal a priori knowledge of the incoming data. The adaptive synchronization algorithms, implemented on a small computer, are capable of accurately estimating pulse transition time, pulse amplitude, and pulse width by invoking the powerful Gauss-Markov theorem. Numerous simulations indicate that the performance of the synchronizer is satisfactory from the point of view of the amount of data required and the speed of convergence.

Chow, L. R.↗
NASA NTRS2018

Identification of Security related Bug Reports via Text Mining using Supervised and Unsupervised Classification

This paper is focused on automated classification of software bug reports to security and non-security related, using both supervised and unsupervised approaches. For both approaches, three types of feature vectors are used. For supervised learning, we experiment with multiple learning algorithms and training sets with different sizes. Furthermore, we propose a novel unsupervised approach based on anomaly detection. The evaluated is based on three NASA datasets. The results show that supervised classification is affected more by the learning algorithms than by feature vectors and using only 25% of the data for training provides as good results as if 90% of data are used for training. Both supervised and unsupervised learning can be used for identification of security bug reports; the former slightly outperforms the latter at the expense of labeling the testing set. In general, the performance differs across datasets, mainly due to the different amounts of security related information.

Goseva-Popstojanova, Katerina↗
DOE OSTI2025

Higher-order factorization machine for accurate surrogate modeling in material design

Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2 nd -order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3 rd -order FM model, capable of modeling three-variable interactions and more intricate relationships in material systems. We comprehensively evaluate the surrogate modeling performance of the 3 rd -order FM case using various objective functions. Furthermore, we examine the optimization reliability and efficiency of the 3 rd -order FM-based active learning in a real-world material design task (e.g., nanophotonic structures for transparent radiative cooling). Our study shows that the 3 rd -order FM outperforms the 2 nd -order model in both surrogate accuracy and optimization performance, highlighting higher-order models’ promises for material design and optimization problems.

Factorization machine↗
NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗
NASA NTRSDate not supplied

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts.1,2 The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners.1 However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen M Middleton↗
DOE OSTI2024

Third-integer Resonant Extraction Regulation System for Mu2e

A third-integer resonant slow extraction system is being developed for Fermilab's Delivery Ring to deliver protons to the upcoming Mu2e experiment. The timescale of the extraction (or spill) duration is 43 milliseconds, which is extremely short and unprecedented. Additionally, the experiment's strict and challenging requirements on the quality of the spill at this time scale has led to the development of a new Spill Regulation System (SRS) design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content suppressor. Contributions to the first two components of the SRS, i.e., Slow Regulation and Fast Regulation subsystems, will be presented in which new adaptive learning algorithm schemes for the slow regulation of the spill -- validated using particle tracking simulations -- shall be described. In addition to these novel methods for the enhancement of the spill regulation system, results of employing Machine Learning in enhancing the performance of the resonant extraction are also presented. At the forefront of applying ML techniques to solve non-linear accelerator control problems, this work includes optimizing the PID gains as well as the replacement of the traditional PID controller using Recurrent Neural Networks and Gated Recurrent Unit (GRU) ML models to achieve efficiencies greater than a PID controller. Cutting-edge on-going Reinforcement Learning efforts, including an actor-critic family of learning algorithms, to regulate the spill rate will be reviewed, as well as present analytical calculations pertaining the transit time of particles in a third-integer resonant extraction. Detailed numerical investigations and validations of such calculations, the model of which could be exported and reliably used in future analytical modeling of any resonant extraction, are discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗
NASA NTRS1987

A learning controller for nonrepetitive robotic operation

A practical learning control system is described which is applicable to complex robotic and telerobotic systems involving multiple feedback sensors and multiple command variables. In the controller, the learning algorithm is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space, rather than learning the actuator commands required to perform a specific task. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. The desired sensor output changes may result from automatic trajectory planning or may be derived from interactive input from a human operator. The learning controller requires no a priori knowledge of the relationships between the sensor outputs and the command variables. The algorithm is well suited for real time implementation, requiring only fixed point addition and logical operations. The results of learning experiments using a General Electric P-5 manipulator interfaced to a VAX-11/730 computer are presented. These experiments involved interactive operator control, via joysticks, of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm.

Miller, W. T., III↗