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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Solar fine scale structures in the corona, transition region, and lower atmosphere

The American Science and Engineering Soft X-ray Imaging Payload and the Naval Research Laboratory High Resolution Telescope and Spectrograph (HRTS) were launched from White Sands on 1987 December 11 in coordinated sounding rocket flights. The goal was to investigate the correspondence of fine-scale structures from different temperature regimes in the solar atmosphere, and particularly the relationship between X-ray bright points (XBPs) and transition region explosive events. We present results of the analysis of co-aligned X-ray images, maps of sites of transition region explosive events observed in C IV 10(exp 5), HRTS 1600 A spectroheliograms of the T(sub min) region, and ground-based magnetogram and He I 10830 A images. We examined the relationship of He I 10830 A dark features and evolving magnetic features which correspond to XBPs. We note a frequent double ribbon pattern of the He I dark feature counterparts to XBPs. We discuss an analysis of the relationship of XBPs to evolving magnetic features by Webb et al., which shows that converging magnetic features of opposite polarity are the most significant magnetic field counterparts to XBPs. The magnetic bipolar features associated with XBPs appear as prominent network elements in chromospheric and transition region images. The features in C IV observations corresponding to XBP sites are in general bright, larger scale (approximately 10 arcsec) regions of complex velocity fields of order 40 km/s, which is typical of brighter C IV network elements. These C IV features do not reach the approximately 100 km/s velocities seen in the C IV explosive events. Also, there are many similar C IV bright network features without a corresponding XBP in the X-ray image. The transition region explosive events do not correspond directly to XBPs. The explosive events appear to be concentrated in the quiet Sun at the edges of strong network, or within weaker field strength network regions. We find a greater number of C IV events than expected from the results of a previous Spacelab 2 HRTS disk survey. We attribute this at least partly to better spatial resolution with the newer HRTS data. The full-disk X-ray image shows a pattern of dark lanes in quiet Sun areas. The number density of C IV events is twice as large inside as outside a dark lane (4.6 x 10(exp -3) vs. 2.3 x 10(exp -3) explosive events per arcsec (exp 2)). The dark lane corresponds to an old decaying magnetic neutral line. We suggest that this provides an increased opportunity for small-scale convergence and reconnection of opposite polarity magnetic field features, in analogy with the results of Webb et al. for XBPs but at a reduced scale of reconnection.

Moses, Dan↗
DOE OSTI2022

Entangling Quantum Generative Adversarial Networks

Generative adversarial networks (GANs) are one of the most widely adopted machine learning methods for data generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (an entangling quantum GAN, EQ-GAN) that overcomes limitations of previously proposed quantum GANs. Leveraging the entangling power of quantum circuits, the EQ-GAN converges to the Nash equilibrium by performing entangling operations between both the generator output and true quantum data. In the first multiqubit experimental demonstration of a fully quantum GAN with a provably optimal Nash equilibrium, we use the EQ-GAN on a Google Sycamore superconducting quantum processor to mitigate uncharacterized errors, and we numerically confirm successful error mitigation with simulations up to 18 qubits. Finally, we present an application of the EQ-GAN to prepare an approximate quantum random access memory and for the training of quantum neural networks via variational datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Safety Assessment of a Machine Learning-Based Aircraft Emergency Braking System: A Case Study

Machine Learning (ML) is revolutionizing many technological fields, but its use in aviation remains restricted due to stringent certification requirements. Efforts by the aviation community to establish standards for certifying ML-based systems are progressing, yet challenges persist, particularly with safety assessment methods for ML-based systems. This research addresses these challenges through a case study of an autonomous emergency braking system utilizing a computer vision deep neural network (DNN). We demonstrate a safety assessment process tailored to ML-specific concerns, such as low integrity and performance variability in quantitative safety analysis. This study can serve as an illustrative example to facilitate the discussion and convergence on certification aspects for ML-based systems within the aviation community.

Safety certification↗
DOE OSTI2023

Accelerating gradient descent and Adam via fractional gradients

Here we propose a class of novel fractional-order optimization algorithms. We define a fractional-order gradient via the Caputo fractional derivatives that generalizes integer-order gradient. We refer it to as the Caputo fractional-based gradient, and develop an efficient implementation to compute it. A general class of fractional-order optimization methods is then obtained by replacing integer-order gradients with the Caputo fractional-based gradients. To give concrete algorithms, we consider gradient descent (GD) and Adam, and extend them to the Caputo fractional GD (CfGD) and the Caputo fractional Adam (CfAdam). We demonstrate the superiority of CfGD and CfAdam on several large scale optimization problems that arise from scientific machine learning applications, such as ill-conditioned least squares problem on real-world data and the training of neural networks involving non-convex objective functions. Numerical examples show that both CfGD and CfAdam result in acceleration over GD and Adam, respectively. We also derive error bounds of CfGD for quadratic functions, which further indicate that CfGD could mitigate the dependence on the condition number in the rate of convergence and results in significant acceleration over GD.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
DOE OSTI2024

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. Finally, this versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Covariance Shaping Over Riemannian Manifolds for Massive MIMO Communication

Acquiring accurate instantaneous channel state information (CSI) is a challenging aspect of massive multi-input multi-output (MIMO) communication. Utilizing statistical information, such as channel covariance matrix, to design statistical beamforming vectors is robust when compared to instantaneous CSI. In this paper, we propose a novel MIMO covariance shaping scheme over Riemannian manifolds. It serves as an effective statistical beamforming solution to a number of close proximity user equipment (UE) that are undergoing substantial channel correlation. Proposed algorithm exploits the Hermitian positive definite nature of covariance matrices lying over Riemannian manifold. We introduce Wasserstein distance function as a Riemannian metric to measure distances between channel covariance matrices. Furthermore, K-means clustering technique is utilized to effectively identify the optimal shape of effective optimal covariance matrices. Our findings suggest that maximizing the geodesic distance between covariance matrices ultimately leads to a corresponding increase in the network throughput, as determined by the beamforming vector used to shape the covariance matrices. Simulation results validate that the proposed solution converges faster than Euclidean-based state-of-the-art, while maintaining the same computational complexity. Finally, the sum rate performance asymptotically achieves full capacity for two-UE case and more than 96% of the upper bound exhaustive search benchmark for multi-UE scenario.

42 ENGINEERING↗
DOE OSTI2023

Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix

This paper delves into designing feedback control gains for a continuous-time linear quadratic regulator (LQR) problem that is constrained to certain predefined structure with unknown state matrix. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. Here we first formulate a model-based framework using dynamic programming (DP) to embed the structural constraint to the LQR gain computation in the continuous-time setting, and then subsequently, formulate a policy iteration RL algorithm that can alleviate the requirement of known state matrix in conjunction with maintaining the feedback gain structure. The design enables a distributed learning control design which is necessary for many large-scale cyber-physical systems. Theoretical guarantees are provided for stability and convergence of the structured reinforcement learning (SRL) algorithm. We validate our theoretical results with numerical simulations on a multi-agent networked linear time-invariant (LTI) dynamic system.

42 ENGINEERING↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

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

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

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Interplanetary Overlay Network Bundle Protocol Implementation

The Interplanetary Overlay Network (ION) system's BP package, an implementation of the Delay-Tolerant Networking (DTN) Bundle Protocol (BP) and supporting services, has been specifically designed to be suitable for use on deep-space robotic vehicles. Although the ION BP implementation is unique in its use of zero-copy objects for high performance, and in its use of resource-sensitive rate control, it is fully interoperable with other implementations of the BP specification (Internet RFC 5050). The ION BP implementation is built using the same software infrastructure that underlies the implementation of the CCSDS (Consultative Committee for Space Data Systems) File Delivery Protocol (CFDP) built into the flight software of Deep Impact. It is designed to minimize resource consumption, while maximizing operational robustness. For example, no dynamic allocation of system memory is required. Like all the other ION packages, ION's BP implementation is designed to port readily between Linux and Solaris (for easy development and for ground system operations) and VxWorks (for flight systems operations). The exact same source code is exercised in both environments. Initially included in the ION BP implementations are the following: libraries of functions used in constructing bundle forwarders and convergence-layer (CL) input and output adapters; a simple prototype bundle forwarder and associated CL adapters designed to run over an IPbased local area network; administrative tools for managing a simple DTN infrastructure built from these components; a background daemon process that silently destroys bundles whose time-to-live intervals have expired; a library of functions exposed to applications, enabling them to issue and receive data encapsulated in DTN bundles; and some simple applications that can be used for system checkout and benchmarking.

Burleigh, Scott C.↗
NASA NTRS1990

Satellite observations of ozone near tropopause folds during the 1982 Atmospheric Variability Experiment

The relationship between the total column ozone and the frontal structure associated with mid-latitude baroclinic waves and jets was investigated by comparing the midday ozone data from the Nimbus-7 TOMS instrument to the upper level analyses of baroclinic zones from the 1982 Atmospheric Variability Experiment (AVE82) rawinsonde network. It was found that, for all the three baroclinic waves captured during the AVE82 campaign, the satellite-observed ozone enhancements were associated with tropopause depressions; however, the ozone maxima occurred 300-500 km to the east of the lowered or folded tropopause, indicating that a stratospheric convergence zone exists well above and eastward of the corresponding wave in the troposphere. Therefore, tropopause folds cannot be considered as the 'cause' of the local ozone maxima above the mid-latitude baroclinic zones captured by the AVE82 network.

Chesters, Dennis↗
NASA NTRS2004

A Subsonic Aircraft Design Optimization With Neural Network and Regression Approximators

The Flight-Optimization-System (FLOPS) code encountered difficulty in analyzing a subsonic aircraft. The limitation made the design optimization problematic. The deficiencies have been alleviated through use of neural network and regression approximations. The insight gained from using the approximators is discussed in this paper. The FLOPS code is reviewed. Analysis models are developed and validated for each approximator. The regression method appears to hug the data points, while the neural network approximation follows a mean path. For an analysis cycle, the approximate model required milliseconds of central processing unit (CPU) time versus seconds by the FLOPS code. Performance of the approximators was satisfactory for aircraft analysis. A design optimization capability has been created by coupling the derived analyzers to the optimization test bed CometBoards. The approximators were efficient reanalysis tools in the aircraft design optimization. Instability encountered in the FLOPS analyzer was eliminated. The convergence characteristics were improved for the design optimization. The CPU time required to calculate the optimum solution, measured in hours with the FLOPS code was reduced to minutes with the neural network approximation and to seconds with the regression method. Generation of the approximators required the manipulation of a very large quantity of data. Design sensitivity with respect to the bounds of aircraft constraints is easily generated.

Patnaik, Surya N.↗
DOE OSTI2023

Deep-learning-aided extraction of optical constants in scanning near-field optical microscopy

Scanning near-field optical microscopy is one of the most effective techniques for spectroscopy of nanoscale systems. However, inferring optical constants from the measured near-field signal can be challenging because of a complicated and highly nonlinear interaction between the scanned probe and the sample. Conventional fitting methods applied to this problem often suffer from the lack of convergence or require human intervention. Here, we develop an alternative approach where the optical parameter extraction is automated by a deep learning network. The network provides an initial estimate that is subsequently refined by a traditional fitting algorithm. In conclusion, we show that this method demonstrates superior accuracy, stability against noise, and computational speed when applied to simulated near-field spectra.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1984

Gravity waves from the stratosphere to the mesosphere, part 1.3B

The propagation of gravity waves from the stratosphere to the mesosphere has important implications both for observers and those who are attempting to parameterize wave breaking in global models. As they propagate from the tropopause to their breaking level (here, assumed to be the mesosphere), gravity waves can encounter a refractive environment since the vertical group velocity is a function of the background wind. They may be focussed or scattered or dissipated before reaching the mesosphere. It is even conceivable that gravity waves may break stop breaking, and begin breaking again at high altitudes with a resultant loss of wave energy in the intervening region. From a modeling viewpoint, the important concern for large-scale flows is the total upward flux of gravity wave (pseudo) momentum entering the stratosphere and mesosphere. The refraction of gravity waves also presents a difficult problem for observers since waves passing through the tropopause may arrive a thousand kilometers upstream in the mesosphere. Since mesosphere - stratosphere - troposphere (MST) radars sense tropospheric and mesospheric conditions most accurately, they are ideally suited to assess the total gravity-wave flux through the tropopause and stratospause. Networks of radars making coordinated measurements may be required to accurately determine the upward flux of momentum as well as the flux convergence between layers.

Schoeberl, M. R.↗
NASA NTRS2000

Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks

Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or clusters of genes with common activity profiles may overlook the most critical features of cellular information processing which normally determine how signal specificity is established and maintained in living cells. Copyright 2000 Academic Press.

Review↗
NASA NTRS2010

High-Performance CCSDS Encapsulation Service Implementation in FPGA

The Consultative Committee for Space Data Systems (CCSDS) Encapsulation Service is a convergence layer between lower-layer space data link framing protocols, such as CCSDS Advanced Orbiting System (AOS), and higher-layer networking protocols, such as CFDP (CCSDS File Delivery Protocol) and Internet Protocol Extension (IPE). CCSDS Encapsulation Service is considered part of the data link layer. The CCSDS AOS implementation is described in the preceding article. Recent advancement in RF modem technology has allowed multi-megabit transmission over space links. With this increase in data rate, the CCSDS Encapsulation Service needs to be optimized to both reduce energy consumption and operate at a high rate. CCSDS Encapsulation Service has been implemented as an intellectual property core so that the aforementioned problems are solved by way of operating the CCSDS Encapsulation Service inside an FPGA. The CCSDS En capsula tion Service in FPGA implementation consists of both packetizing and de-packetizing features

Clare, Loren P.↗