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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
DOE OSTI2022

An Efficient, Scalable IO Framework for Sparse Data: larcv3

Neutrino physics is one of the fundamental areas of research into the origins and properties of the Universe. Many experimental neutrino projects use sophisticated detectors to observe properties of these particles, and have turned to deep learning and artificial intelligence techniques to analyze their data. From this, we have developed \texttt{larcv}, a \texttt{C++} and \texttt{Python} based framework for efficient IO of sparse data with particle physics applications in mind. We describe in this paper the \texttt{larcv} framework and some benchmark IO performance tests. \texttt{larcv} is designed to enable fast and efficient IO of ragged and irregular data, at scale on modern HPC systems, and is compatible with the most popular open source data analysis tools in the Python ecosystem.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data

Artificial intelligence (AI) applications driven by machine learning (ML) are transformational technologies within the international nuclear security regime. Advancements realized by AI—faster and improved data insights, more efficient and automated processes, reductions in human error—enable nuclear security applications such as behavior analysis for insider threat mitigation, source tracking of stolen nuclear material, and facial recognition software for physical protection. In addition to the advantages, however, there are also inherent vulnerabilities and threats associated with its use and risk mitigations must be built into any AI/ML-enabled systems. This work provides a background on AI and ML and different data types used in the field, including open-source intelligence information (OSINT) that is discoverable by AI tools and application data that are used by AI tools for decision-making and automation. Current and potential AI applications and vulnerabilities related to their use within the nuclear security regime are also discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Foreword to special issue: Papers from the 63rd annual meeting of the APS Division of Plasma Physics, November 8–12, 2021

The 63rd annual meeting of the APS Division of Plasma Physics (DPP) was held on November 8–12, 2021 in Pittsburgh at the David Lawrence Convention Center with both a live (in person) component and a virtual component. Following guidance from an APS COVID task force, all in-person attendees were fully vaccinated and masked. More than 800 physicists attended, safely, in-person. With both virtual and on-site participants, discussions were lively, and the research presentations showed unmatched mastery in the modern observation, theory, simulation, and manipulation of plasma. The presentations included four invited review talks, 97 invited talks, four tutorials, and four presentations from this year's prize and award recipients. There were more than 1200 contributed poster presentations and 725 contributed oral presentations. Including both in-person and remote attendees, DPP 2021 had a record of 2232 participants. As a hybrid meeting, in-person presentations of all invited presentations were broadcast live and were accompanied by a Q&A discussion. Contributed oral and poster presentations were prerecorded along with options to schedule in-person discussions on demand. Five mini-conferences were held: “Gatekeeper Workshop: Creating a Diverse, Equitable, and Inclusive Pipeline,” “Collisionless Shocks in Laboratory and Space Plasmas,” “The High Repetition Rate Frontier in High-Energy-Density Physics,” “Measuring and Modeling Plasma Surface Interactions,” and “The Second Mini-Conference on Machine Learning, Data Science and Artificial Intelligence in Plasma Research.” Finally, on the day before the official start of the meeting, an afternoon “for students, by students” included lightning talks, plasma trivia, and an informal occasion to connect with other students, learn how to get the most from the DPP Annual Meeting, and share successful ways to connect with colleagues and advance their professional careers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate. To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architecture which is based on a feature space shared by the low- and high-fidelity solutions. In the feature space, the projections of the low-fidelity and high-fidelity solutions are adjacent by constraining their relative distance. The feature space is represented with an encoder and its mapping to the original solution space is effected through a decoder. In conclusion, the proposed multi-fidelity approach is validated on forward and inverse problems for steady and unsteady problems described by partial differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Foundational Science to Accelerate Nuclear Energy Innovation [Brochure]

The foundational science gaps inhibiting the advancement of nuclear energy technologies are identified and tackled in five priority research opportunities. These opportunities pave the way to accelerate the development and ultimately the adoption of new nuclear energy systems. They include the fundamental aspects of ion-electron interactions; novel properties of next-generation coolants and solvents; interfacial dynamics, not only in solids, but in other aspects of nuclear reactors; novel operando and in-situ monitoring and sensing; and artificial intelligence to accelerate condensed phases discovery. Building on the foundation established by previous BES workshops, these opportunities encompass recent advances in fundamental knowledge and focus on the experimental and computational methods needed to resolve major technical challenges for nuclear energy technologies. Through developing fundamental scientific insight as well as pushing the frontiers of modeling complex systems and probing the operation of materials and chemical systems in extreme environments, research motivated by the priorities identified here will further develop the promise, potential, and utilization of nuclear energy for a clean energy future. The PROs are as follows: (1) Master complex electronic structures to tailor thermochemical reactivity, transport, and microstructural evolution; (2) Interrogate and direct the physics and chemistry underpinning next-generation coolants and solvents; (3) Elucidate and control the underlying physics and chemistry of interfaces in complex nuclear environments; (4) Bridge multi-fidelity multi-resolution experiments, computational modeling, and data science to control dynamic behavior; and (5) Harness artificial intelligence to design inherently resilient condensed phases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Revealing the interplay between “intelligent behavior” and surface reconstruction of non-precious metal doped SrTiO 3 catalysts during methane combustion

The impact of surface reconstruction of a model perovskite, SrTiO 3 (STO), on CH 4 activation for combustion and oxidative coupling was previously revealed that the reaction rate was proportional to the creation of Srterminated step sites. Doped perovskites (SrTi 1-x M x O 3 , M=metal dopant) present yet another form of reconstruction throughout the surface and the bulk, where the metal dopant can migrate in and out of the perovskite lattice, also known as "intelligent behavior". In this work, understanding the interplay between perovskite surface reconstruction (surface termination) and the "intelligent behavior" is tackled for the first time, and the catalytic consequences are probed with CH 4 combustion as a model reaction. A set of experimental techniques, including XRD, Raman spectroscopy, X-ray adsorption spectroscopy, kinetic measurements, as well as DFT calculations were used to understand the catalytic behavior of the reconstructed surfaces of Ni and Cu-doped STO for methane combustion. Here, we found that during methane oxidation, the diffusion of Ni and Cu into the lattice due to the "intelligent behavior" is accompanied by Sr enrichment on the surface of the perovskite. This Srenrichment process is reversible when Cu or Ni species exsolute as clusters/nanoparticles upon H 2 treatment. Such a surface reconstruction is found to greatly impact the catalytic activity of doped perovskites towards methane combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models

Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Structure–Property Linkage in Alloys Using Graph Neural Network and Explainable Artificial Intelligence

Deep learning tools have recently shown significant potential for accelerating the prediction of microstructure–property linkage in materials. While deep neural networks like convolution neural networks (CNNs) can extract physics information from 3D microstructure images, they often require a large network architecture and substantial training time. In this research, we trained a graph neural network (GNN) using phase field generated microstructures of Ni-Al alloys to predict the evolution of mechanical properties. We found that a single GNN is capable of accurately predicting the strengthening of Ni-Al alloys with microstructures of varying sizes and dimensions, which cannot otherwise be done with a CNN. Additionally, GNN requires significantly less GPU utilization than CNN and offers more interpretable explanation of predictions using saliency analysis as features are manually defined in the graph. We also utilize explainable artificial intelligence tool Bayesian Inference to determine the coefficients in the power law equation that governs coarsening of precipitates. Overall, our work demonstrates the ability of the GNN to accurately and efficiently extract relevant information from material microstructures without having restrictions on microstructure size or dimension and offers an interpretable explanation.

Chemistry↗
DOE OSTI2023

FAIR AI models in high energy physics

Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Observation and Control of Unidirectional Ballistic Dynamics of Nanoparticles at a Liquid–Gas Interface by 4D Electron Microscopy

Understanding and controlling the dynamics of active Brownian objects far from equilibrium are fundamentally important for emerging technologies such as artificial micro/nanomotors for drug deliveries and noninvasive microsurgery. However, direct observation and control of unidirectional propulsion of individual nanoscale objects are technically challenging due to the required spatiotemporal resolution. Here, we report in situ visualization and manipulation of unidirectional superfast ballistic dynamics of a single-photon-activated gold nanoparticle (NP) along the liquid–gas interface by four-dimensional electron microscopy (4D EM) at nanometer and nanosecond scales. We observed that, upon repetitive femtosecond laser excitation, the NP at the liquid–gas interface exhibits a continuously superfast unidirectional translation with a linear dependence of its root mean squared velocity (νrms) on either the laser fluence or repetition rate. Under a single femtosecond pulse excitation, the NP exhibits a superfast ballistic translation at the nanosecond time scale. Combined experiment and physical modeling reveals that the superfast unidirectional, ballistic translation is driven by unidirectional random impulsive forces arising from the nanobubbles (NBs) induced by enhanced laser heating as a result of plasmonic excitation, which is controllable by tuning the laser characteristics. Furthermore, this directional plasmonic NB-propulsion mechanism sheds light on the design of light-controllable artificially intelligent micro/nanomotor systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2024

eGridGPT: Trustworthy AI in the Control Room

This report outlines the synergy between human decision making and generative artificial intelligence (GenAI), where GenAI supports power system operators by analyzing procedures, suggesting actions, simulating scenarios with physics-based digital twins, and recommending optimal decisions. This report is the first research effort to apply large language models (LLMs), a type of GenAI, in the power grid control room. The authors describe the Electric Grid Generative Pretrained Transformer (eGridGPT), an LLM that virtually assists system operators. Developed with cybersecurity and regulatory requirements in mind, eGridGPT represents an opportunity to responsibly evolve control room technologies to meet the needs of a rapidly changing grid. As an innovative concept, eGridGPT seeks to spearhead productive discussions about the advanced technologies in the control room of the future amid the transition to clean energy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

SAFARI - Secure Automation For Advanced Reactor Innovation (Final Technical Report)

The Secure Automation For Advanced Reactor Innovation (SAFARI) project was a pioneering initiative aimed at fundamentally changing how nuclear power plants are operated and maintained. Recognizing that current nuclear plants often rely on extensive manual procedures and large staffs, leading to higher costs compared to other energy sources like natural gas, SAFARI sought to introduce smart, automated technologies to make nuclear energy more efficient, cost-effective, and safer. This report details the research and development efforts of the SAFARI project, bringing us closer to a future where advanced nuclear reactors can operate more autonomously, adapt flexibly to energy demands, and predict their maintenance needs before issues arise. One of the key achievements of the SAFARI project is its contribution to our understanding of how Artificial Intelligence (AI) and sophisticated computer models, known as Digital Twins, can be effectively integrated with the complex physics of nuclear reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1988

Applications of artificial intelligence to space station: General purpose intelligent sensor interface

This final report describes the accomplishments of the General Purpose Intelligent Sensor Interface task of the Applications of Artificial Intelligence to Space Station grant for the period from October 1, 1987 through September 30, 1988. Portions of the First Biannual Report not revised will not be included but only referenced. The goal is to develop an intelligent sensor system that will simplify the design and development of expert systems using sensors of the physical phenomena as a source of data. This research will concentrate on the integration of image processing sensors and voice processing sensors with a computer designed for expert system development. The result of this research will be the design and documentation of a system in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, television camera selection, voice recognition hardware selection, or analog signal processing. The user will be able to access data from video or voice sensors through standard LISP statements without any need to know about the sensor hardware or software.

Mckee, James W.↗
NASA NTRS1988

Telerobotics for the efficient utilization of space

Telerobotics is a new technology that is being developed to perform remote manipulation in space. It seeks to accommodate the needs arising from a rapidly increasing investment in space assets and from the large costs deriving from human operations in space, especially EVA. Telerobotics applies advanced automation and artificial intelligence technology and combines the immediacy of execution of teleoperation (the replication at a distance of the physical motions of the operator) with the efficiency and precision of supervised robotic autonomy (the accomplishment of assignments through machine task decomposition and interpretation of sensor information). One of the key goals of this approach is the achievement of a seamless transition between teleoperation and supervised autonomy. The basis of this technology and of the NASA telerobotics research and development activities are described. They consist of five elements: core research, laboratory integration testbed, mission analysis, application demonstrations and flight experiments. Current advances, both in research and system integration are reported including the first integration of the laboratory testbed and quantitative comparisons obtained in a buoyant facility. Planned future goals are outlined.

Varsi, Giulio↗
NASA NTRS1990

General purpose intelligent sensor interface

The long range goal is to develop an intelligent sensor system that will simplify the design and development of expert systems that use sensors of physical phenomena as a source of input data. This phase of the research concentrated on the integration of image processing sensors with expert system environments. The anticipated result of this research is the ability to design systems in which the user will not need to be an expert in such areas as image processing algorithms, local area networks, image processor hardware selection or interfacing, or television cameras selection. The user will be able to access data from video sensors through standard expert system statements without any need to know about the sensor hardware or software.

Mckee, J. W.↗
DOE OSTI2025

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors

We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped-out in 28nm TSMC CMOS bulk process, which has been shown to have sufficient radiation hardness for particle experiments. This development represents a key step toward enabling fully on-detector edge AI, with broad implications for data throughput and discovery potential in high-rate, high-radiation environments such as the High-Luminosity LHC.

Parpillon, Benjamin [Fermilab; Illinois U., Chicag↗