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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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NASA NTRS1999

Detection in fixed and random noise in foveal and parafoveal vision explained by template learning

Foveal and parafoveal contrast detection thresholds for Gabor and checkerboard targets were measured in white noise by means of a two-interval forced-choice paradigm. Two white-noise conditions were used: fixed and twin. In the fixed noise condition a single noise sample was presented in both intervals of all the trials. In the twin noise condition the same noise sample was used in the two intervals of a trial, but a new sample was generated for each trial. Fixed noise conditions usually resulted in lower thresholds than twin noise. Template learning models are presented that attribute this advantage of fixed over twin noise either to fixed memory templates' reducing uncertainty by incorporation of the noise or to the introduction, by the learning process itself, of more variability in the twin noise condition. Quantitative predictions of the template learning process show that it contributes to the accelerating nonlinear increase in performance with signal amplitude at low signal-to-noise ratios.

NASA Center ARC↗

NASA NTRS1999

Reinforcement Learning in a Nonstationary Environment: The El Farol Problem

This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.

Bell, Ann Maria↗

DOE OSTI2025

Parametric matrix models

We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate physical systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation.

Computational science↗

NASA NTRS2008

The Costs of Knowledge

Acquiring knowledge-genuinely learning something new-requires the consent and commitment of the person you're trying to learn from. In contrast to information, which can usually be effectively transmitted in a document or diagram, knowledge comes from explaining, clarifying, questioning, and sometimes actually working together. Getting this kind of attention and commitment often involves some form of negotiation, since even the most generous person's time and energy are limited. Few experts sit around waiting to share their knowledge with strangers or casual acquaintances. In reasonably collaborative enterprises- I think NASA is one-this sort of negotiation isn't too onerous. People want to help each other and share what they know, so the "cost" of acquiring knowledge is relatively low. In many organizations (and many communities and countries), however, there are considerable costs associated with this activity, and many situations in which negotiations fail. The greatest knowledge cost is in and adopting knowledge to one's own use. Sometimes this means formally organizing what one learns in writing. Sometimes it means just taking time to reflect on someone else's thoughts and experiences-thinking about knowledge that is not exactly what you need but can lead you to develop ideas that will be useful. A long, discursive conversation, with all the back-and-forth that defines conversation, can be a mechanism of knowledge exchange. I have seen many participants at NASA APPEL Masters Forums talking, reflecting, and thinking-adapting what they are hearing to their own needs. Knowledge transfer is not a simple proposition. An enormous amount of information flows through the world every day, but knowledge is local, contextual, and "stickyn-that is, it takes real effort to move it from one place to another. There is no way around this. To really learn a subject, you have to work at it, you have to pay your "knowledge dues." So while, thanks to advances in technology, almost infinite amounts of information are instantly available, it still takes the same amount of time and work to learn French as it did in the year 1800-or to master physics or philosophy.

Prusak, Laurence↗

DOE OSTI2024

Deep Learning for Spectroscopic X-ray Nano-Imaging Denoising

Synchrotron transmission X-ray microscopy with absorption near edge structure (TXM-XANES) is a powerful tool for investigating the structure and composition of materials at nano- to meso-scales. It is, however, often challenged by high levels of noise that obscure critical details at the single-pixel level. To address this issue, a deep learning-based algorithm is developed for suppressing the image noise, grounded in self-supervised learning principles. In contrast to traditional image denoising methods, this approach successfully enhances the visibility of fine details while significantly reducing the noise in the X-ray images. Through this advancement, the potential of the approach for improving the accuracy and interpretability of the TXM-XANES data is demonstrated, thereby enabling more precise detection of nanoscale phenomena such as inhomogeneous cation redox and metal segregation in battery cathode materials. This technique offers an effective new avenue for harnessing the full potential of synchrotron TXM-XANES imaging, paving the way for a range of exciting new studies in materials science and beyond.

36 MATERIALS SCIENCE↗

NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob↗

NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

Hageman, Jacob J.↗

DOE OSTI2025

Graph-based Reversible Evaluation and Tangents Library

GRETL is a C++ library for evaluation, re-evaluation and algorithmic differentiation of functional operations on an arbitrary computational graph with limited memory usage. Similar to popular machine learning frameworks in Python, like PyTorch and JAX, it tracks and stores both operations and output data as functions are evaluated. Once this composition of functions is built up, the entire chain of operations can be back propagated to compute sensitivities of the final result with respect to any number of inputs. In contrast to most machine learning applications, memory usage becomes the bottleneck for back propagation in many physics applications, especially for time-dependent PDEs. Dynamic check pointing becomes essential. An important distinguishing feature of GRETL is its ability to limit the maximum memory usage by automatically dynamic checkpointing the data output for each graph operation (see Wang, Moin, Iaccarino, 2009). During backpropagation, parts of the graph that are no longer in memory are automatically re-evaluated from upstream checkpointed states as needed for derivative sensitivity calculations (or more precisely, for vector-Jacobian products). GRETL is particularly beneficial for applications, such as coupled multi-physics, where deriving adjoint-based sensitivities and managing checkpoint memory across modules becomes onerous. Cases which can be readily handled by the GRETL library include: different time-integration algorithms per physics (e.g., coupled predictor-corrector algorithms, IMEX, etc.), sub-cycling, asynchronous integrators, state dependent timestep sizes, iterative solvers and coupling algorithms, controller algorithms, and more.

Tupek, MichaelR [Lawrence Livermore National Labor↗

NASA NTRS2017

The CHARIS High-Contrast Integral-Field Spectrograph

One of the leading direct Imaging techniques, particularly in ground-based imaging, uses a coronagraphic system and integral field spectrograph (IFS). The Coronagraphic High Angular Resolution Imaging Spectrograph (CHARIS) is an IFS that has been built for the Subaru telescope. CHARIS has been delivered to the observatory and now sits behind the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system. CHARIS has 'high' and 'low' resolution operating modes. The "high-resolution" mode is used to characterize targets in J, H, and K bands at ~R70. The "low-resolution" prism is meant for discovery and spans J+H+K bands (1.15-2.37 microns) with a spectral resolution of ~R18. This discovery mode has already proven better than 15-sigma detections of HR8799c,d,e when combining ADI+SDI. Using SDI alone, planets c and d have been detected in a single 24 second image. The CHARIS team is optimizing instrument performance and refining ADI+SDI recombination to maximize our contrast detection limit. In addition to the new observing modes, CHARIS has demonstrated a design with high robustness to spectral crosstalk. CHARIS is in the final stages of commissioning, with the instrument open for science observations beginning February 2017. Here we review the science case, design, on-sky performance, engineering observations of exoplanet and disk targets, and specific lessons learned for extremely high contrast imagers. Key design aspects that will be demonstrated are crosstalk optimization, wavefront correction using the IFS image, lenslet tolerancing, the required spectral resolution to fit exoplanet atmospheres, and the utility of the spectrum in achieving higher contrast detection limits.

Groff, Tyler D.↗

DOE OSTI2025

Online energy consumption forecast for battery electric buses using a learning-free algebraic method

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Equivariant Graph Attention Network - 3D Conformers & Feature Fusion

EGAN-3F (Equivariant Graph Attention Network - 3D Conformers & Feature Fusion) presents an innovative approach for predicting binding affinity between small molecules and protein targets, a fundamental task in drug discovery. Traditional structure-based methods often depend on protein-ligand complex structures obtained from crystallography or molecular docking. In contrast, ligand-only machine learning models using 1D or 2D representations such as SMILES have been developed to predict binding affinity without structural information about the target; however, their accuracy is often limited due to the lack of 3D ligand information. EGAN-3F addresses this limitation by integrating spatially aware graph learning with traditional descriptor-based features. We systematically investigate how combining 2D and 3D molecular representations enhances binding affinity prediction from SMILES strings. This approach underscores the importance of modeling conformational diversity and incorporating chemically meaningful descriptors to improve predictive accuracy. The key innovation of EGAN-3F lies in its ability to achieve robust ligand-based binding affinity predictions without requiring protein-ligand complex structures, effectively bridging the gap between purely structural and ligand-only modeling paradigms.

Shim, Heesung [Lawrence Livermore National Laborat↗

NASA NTRS1993

Low melting temperature alloy deployment mechanism and recent experiments

This paper describes the concept of a low melting temperature alloy deployment mechanism, U.S. Patent 4,842,106. It begins with a brief history of conventional dimethyl-silicone fluid damped mechanisms. Design fundamentals of the new melting alloy mechanism are then introduced. Benefits of the new over the old are compared and contrasted. Recent experiments and lessons learned complete this paper.

Madden, M. J.↗

NASA NTRS2013

Crew Roles and Interactions in Scientific Space Exploration

Future piloted space exploration missions will focus more on science than engineering, a change which will challenge existing concepts for flight crew tasking and demand that participants with contrasting skills, values, and backgrounds learn to cooperate as equals. In terrestrial space flight analogs such as Desert Research And Technology Studies, engineers, pilots, and scientists can practice working together, taking advantage of the full breadth of all team members training to produce harmonious, effective missions that maximize the time and attention the crew can devote to science. This paper presents, in a format usable as a reference by participants in the field, a successfully tested crew interaction model for such missions. The model builds upon the basic framework of a scientific field expedition by adding proven concepts from aviation and human spaceflight, including expeditionary behavior and cockpit resource management, cooperative crew tasking and adaptive leadership and followership, formal techniques for radio communication, and increased attention to operational considerations. The crews of future spaceflight analogs can use this model to demonstrate effective techniques, learn from each other, develop positive working relationships, and make their expeditions more successful, even if they have limited time to train together beforehand. This model can also inform the preparation and execution of actual future spaceflights.

Love, Stanley G.↗

DOE OSTI2024

Developing an Interactive OpenMP Book with Large Language Models

Abstract. This paper presents an approach to authoring a textbook titled Interactive OpenMP Programming with the assistance of Large Language Models (LLMs). The writing process utilized state-of-the-art LLMs, including Gemini Pro 1.5, Claude 3, and ChatGPT-4, to generate the initial structure and outline of the book, as well as the initial content for specific chapters. This content included detailed descriptions of individual OpenMP constructs and practical programming examples. The outline and content have then undergone extensive manual revisions to meet our book goals. In this paper, we report our findings about the capabilities and limitations of these LLMs. We address critical questions concerning the necessity of textbook resources and the effectiveness of LLMs in creating fundamental and practical programming content. Our findings suggest that while LLMs offer significant advantages in generating textbook content, they require careful integration with traditional educational methodologies to ensure depth, accuracy, and pedagogical effectiveness. The Interactive OpenMP Programming book is developed with the framework of Jupyter Book, enabling the execution of code within the book from the web browser, providing instant feedback and a dynamic learning experience that stands in contrast to traditional educational resources. The book represents a significant step towards modernizing programming education, offering insights into practical strategies for generating the textbook through advanced AI tools.

Large Language Model · OpenMP · Interactive Book ·↗

NASA NTRS2021

The Twin Decadal Survey Testbeds in the High Contrast Imaging Testbed facility at NASA’s Jet Propulsion Laboratory

Direct imaging of an Earth-like exoplanet will require future missions, such as the proposed HabEx or LUVOIR concepts, to achieve starlight suppression with planet/star contrast ratios on the order of 10−10 at angular separations <100 milliarcseconds in visible wavelengths. To facilitate the technology and algorithm development required to enable this ambitious scientific goal, NASA’s Exoplanet Exploration Program (ExEP) High Contrast Imaging Testbed (HCIT) facility at the Jet Propulsion Laboratory (JPL) recently commissioned the Decadal Survey Testbed (DST), a state-of-the-art, flexible, and extremely stable coronagraph testbed. This testbed integrates years of experience and lessons learned at HCIT and has now demonstrated contrast ratios as low as few times 10−10 in 10% bandwidth. Following the success of DST, the HCIT team has developed a companion testbed, the Decadal Survey Testbed 2 (DST2) that further implements lessons learned from DST, and will provide the HCIT with additional coronagraph testbed capacity and efficiency. Commissioning for DST2 is currently targeted for Fall 2021. Here we provide an update on the status of DST, and report on the design, performance modeling simulations, and status of integration and testing for DST2, highlighting updates to the design from the original DST.

Siegler, Nick↗

DOE OSTI2024

Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots

Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown function given input/output data. Type 2: Consider a collection of variables and functions, some of which are unknown, indexed by the nodes and hyperedges of a hypergraph (a generalized graph where edges can connect more than two vertices). Given partial observations of the variables of the hypergraph (satisfying the functional dependencies imposed by its structure), approximate all the unobserved variables and unknown functions. Type 3: Expanding on Type 2, if the hypergraph structure itself is unknown, use partial observations of the variables of the hypergraph to discover its structure and approximate its unknown functions. These hypergraphs offer a natural platform for organizing, communicating, and processing computational knowledge. While most scientific problems can be framed as the data-driven discovery of unknown functions in a computational hypergraph whose structure is known (Type 2), many require the data-driven discovery of the structure (connectivity) of the hypergraph itself (Type 3). We introduce an interpretable Gaussian Process (GP) framework for such (Type 3) problems that does not require randomization of the data, access to or control over its sampling, or sparsity of the unknown functions in a known or learned basis. Its polynomial complexity, which contrasts sharply with the super-exponential complexity of causal inference methods, is enabled by the nonlinear ANOVA capabilities of GPs used as a sensing mechanism.

Science & Technology - Other Topics↗

NASA NTRSDate not supplied

Holistic and Pragmatic Standards Processes Enable Interdisciplinary Science

Open, interdisciplinary science inevitably relies heavily on standards. Standards are those often unseen agreements that we take for granted when systems and processes are working fine. Yet standards work is perpetual, laborious, and sometimes contentious, especially for standards to work across diverse disciplines. Standards development, maintenance, and implementation is a complex, ongoing socio-technical process. NASA has developed a progressively open science policy and strategy that calls for the establishment of a data standards process reaching across the five diverse divisions of the Science Mission Directorate. This is a delicate exercise. We, therefore, seek to apply a holistic yet pragmatic approach to developing and maintaining a standards process. We adopt an ecological philosophy that focuses on the interactions within the data ecosystem and how standards facilitate those interactions. We couple high-level analysis with on the ground experimentation. We began by 1) mapping information ecosystem components (e.g. data centers, missions, services, protocols, users), 2)establishing how the components interact (e.g. sharing (meta)data, funding, personnel exchange), and 3) modelling system dynamics (e.g. creation of products from multiple data centers, redundant processes, shared services). The goal is to apply understanding of the ecosystem to real world applications (e.g. planning a new mission, implementing new policy requirements, improving process efficiency, etc.). We have also conducted studies of historical standardization efforts, documenting lessons learned and cautionary tales. We then contrast this more abstract work with real examples. We reviewed and assessed multiple existing standards development processes both within and external to NASA. We now work to implement an initial test process which can be further optimized. We seek to define a consistent approach for assigning persistent identifiers for research objects, especially for the purposes of citation. The experience from this relatively ‘simple’ test case adds a pragmatic perspective on how researchers and engineers actually work. This presentation will review the details of this methodology, our initial findings, and how they might apply to other interdisciplinary standardization efforts.

Mark A Parsons↗

DOE OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗