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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗
NASA NTRS1985

Adaptive Inverse Control for Rotorcraft Vibration Reduction

This thesis extends the Least Mean Square (LMS) algorithm to solve the mult!ple-input, multiple-output problem of alleviating N/Rev (revolutions per minute by number of blades) helicopter fuselage vibration by means of adaptive inverse control. A frequency domain locally linear model is used to represent the transfer matrix relating the higher harmonic pitch control inputs to the harmonic vibration outputs to be controlled. By using the inverse matrix as the controller gain matrix, an adaptive inverse regulator is formed to alleviate the N/Rev vibration. The stability and rate of convergence properties of the extended LMS algorithm are discussed. It is shown that the stability ranges for the elements of the stability gain matrix are directly related to the eigenvalues of the vibration signal information matrix for the learning phase, but not for the control phase. The overall conclusion is that the LMS adaptive inverse control method can form a robust vibration control system, but will require some tuning of the input sensor gains, the stability gain matrix, and the amount of control relaxation to be used. The learning curve of the controller during the learning phase is shown to be quantitatively close to that predicted by averaging the learning curves of the normal modes. For higher order transfer matrices, a rough estimate of the inverse is needed to start the algorithm efficiently. The simulation results indicate that the factor which most influences LMS adaptive inverse control is the product of the control relaxation and the the stability gain matrix. A small stability gain matrix makes the controller less sensitive to relaxation selection, and permits faster and more stable vibration reduction, than by choosing the stability gain matrix large and the control relaxation term small. It is shown that the best selections of the stability gain matrix elements and the amount of control relaxation is basically a compromise between slow, stable convergence and fast convergence with increased possibility of unstable identification. In the simulation studies, the LMS adaptive inverse control algorithm is shown to be capable of adapting the inverse (controller) matrix to track changes in the flight conditions. The algorithm converges quickly for moderate disturbances, while taking longer for larger disturbances. Perfect knowledge of the inverse matrix is not required for good control of the N/Rev vibration. However it is shown that measurement noise will prevent the LMS adaptive inverse control technique from controlling the vibration, unless the signal averaging method presented is incorporated into the algorithm.

Jacklin, Stephen A.↗
NASA NTRS1994

Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Buntine, Wray L.↗
NASA NTRS2003

Vergence-dependent adaptation of the vestibulo-ocular reflex

The gain of the vestibulo-ocular reflex (VOR) normally depends on the distance between the subject and the visual target, but it remains uncertain whether vergence angle can be linked to changes in VOR gain through a process of context-dependent adaptation. In this study, we examined this question with an adaptation paradigm that modified the normal relationship between vergence angle and retinal image motion. Subjects were rotated sinusoidally while they viewed an optokinetic (OKN) stimulus through either diverging or converging prisms. In three subjects the diverging prisms were worn while the OKN stimulus moved out of phase with the head, and the converging prisms were worn when the OKN stimulus moved in-phase with the head. The relationship between the vergence angle and OKN stimulus was reversed in the fourth subject. After 2 h of training, the VOR gain at the two vergence angles changed significantly in all of the subjects, evidenced by the two different VOR gains that could be immediately accessed by switching between the diverged and converged conditions. The results demonstrate that subjects can learn to use vergence angle as the contextual cue that retrieves adaptive changes in the angular VOR.

NASA Discipline Developmental Biology↗
NASA NTRS1991

Magellan mission to Venus - Radar system design and operational challenges

The Magellan radar mission to Venus was launched in May 1989 and placed into orbit around Venus in August 1990. After several weeks of check-out including two complete signal loss events from the spacecraft, the mission began collecting data from the radar instrument in the three modes of synthetic aperture, altimetry, and passive radiometry. The author presents an overview of the mission and shows some results, concentrating on the engineering aspects of the results. He also describes how the many challenges were overcome and the system was adapted to the realities of Venus and the radar system that is being used to map the planet. It is concluded that this mission has presented many lessons learned about not only the design of an imaging and altimetric radar system but also the adaptation of this design to the mission operational constraints.

Johnson, William T. K.↗
NASA NTRS2003

SpaceDock: A Performance Task Platform for Spaceflight Operations

Preliminary evidence during both short- and long-duration spaceflight indicates that perceptual-motor coordination changes occur and persist in-flight. However, there is presently no in-flight method for evaluating astronaut performance on mission-critical tasks such as docking. We present a portable platform we have developed for attempting and evaluating docking, and describe the results of a pilot study wherein flight novices learned the docking task. Methods: A dual-joystick, six degrees of freedom platform-called SpaceDock-was developed to enable portable, adaptable performance testing in a spaceflight operations setting. Upon this platform, a simplified docking task was created, involving a constant rate of approach towards a docking target and requiring the user to correct translation in two dimensions and attitude orientation along one dimension (either pitch or roll). Ten flight naive subjects performed the task over a 45 min period and all joystick inputs and timings were collected, from which we could successfully reconstruct travel paths, input profiles and relative target displacements. Results: Subjects exhibited significant improvements in docking over the course of the experiment. Learning to compensate for roll-alterations was robust, whereas compensation for pitch-alterations was not in evidence in this population and relatively short training period. Conclusion: The SpaceDock platform can provide a novel method for training and testing subjects, on a spaceflight-relevant task, and can be used to examine behavioral learning, strategy use, and has been adapted for use in brain imaging experiments.

Marshburn, Thomas H.↗
DOE OSTI2026

Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials

Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

YOLO for Radio Frequency Signal Classification

Radio frequency signal classification plays a pivotal role in various applications, including spectrum management, wireless security, and cognitive radio. Extant signal classification methods require significant data throughput and are not multilabel. We propose a novel approach to radio frequency signal classification by leveraging the You Only Look Once (YOLO) object detection method. YOLO is a state-of-the-art deep learning model renowned for its real-time object detection capabilities in computer vision applications. We adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image. Index Terms—radio-frequency analysis, object detection, neural networks, machine learning, deep learning.

42 ENGINEERING↗
NASA NTRS2022

SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots

Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym ( So ft Mo tion Gym ), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.

Moritz A. Graule↗
NASA NTRSDate not supplied

Monte Carlo Tree Search for Integrated Planning, Learning, and Execution in Nondeterministic Python

We present a novel use of Monte Carlo Tree Search (MCTS),adapted to explore a search space produced by the choice points embedded in Python code. The choice points are non-deterministic assignment statements and subroutine calls. We present MCTS extensions required for doing tree search in this context which includes control constructs like hierarchical decomposition (subroutine calls), iterative while loops and conditional statements. We demonstrate how the system works in a simulated rideshare scenario in an urban setting, and present preliminary experiments as a proof of concept.

Automatic planning↗
NASA NTRS1992

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Berenji, Hamid R.↗
NASA NTRS1993

Attitude control of spacecraft using neural networks

This paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.

Vadali, Srinivas R.↗
NASA NTRS1977

The squirrel monkey as a candidate for space flight

Because of its size and other unique diurnal-primate characteristics, the squirrel monkey is used in: (1) actual bioflight missions, (2) in laboratory tests designed to clarify the risks to man during launch and recovery as well as in hazardous spaceflight environments; and (3) in the acquisition of data on unknown risks encountered in long duration space exploration. Pertinent data concerning samiri sciureus as described in published and unpublished reports are summarized. Topics include: taxonomy, ethology, life history, sensory-learning-motor capabilities in primate perspective, anatomy and physiology (including homeostatic adaptation to stress), susceptibility to environmental hazards, reproduction, care and clinical management, and previous use in aerospace biomedical research.

Brizzee, K. R.↗
NASA NTRS1990

Neural networks for function approximation in nonlinear control

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

Linse, Dennis J.↗
NASA NTRS1992

Using neural networks in software repositories

The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology.

Eichmann, David↗
NASA NTRS2008

Towards Intelligent Control for Next Generation Aircraft

NASA Aeronautics Subsonic Fixed Wing Project is focused on mitigating the environmental and operation impacts expected as aviation operations triple by 2025. The approach is to extend technological capabilities and explore novel civil transport configurations that reduce noise, emissions, fuel consumption and field length. Two Next Generation (NextGen) aircraft have been identified to meet the Subsonic Fixed Wing Project goals - these are the Hybrid Wing-Body (HWB) and Cruise Efficient Short Take-Off and Landing (CESTOL) aircraft. The technologies and concepts developed for these aircraft complicate the vehicle s design and operation. In this paper, flight control challenges for NextGen aircraft are described. The objective of this paper is to examine the potential of state-of-the-art control architectures and algorithms to meet the challenges and needed performance metrics for NextGen flight control. A broad range of conventional and intelligent control approaches are considered, including dynamic inversion control, integrated flight-propulsion control, control allocation, adaptive dynamic inversion control, data-based predictive control and reinforcement learning control.

Acosta, Diana Michelle↗