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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Simultaneous access to high normalized density, current, pressure, and confinement in strongly-shaped diverted negative triangularity plasmas

Strongly-shaped diverted negative triangularity (NT) plasmas in the DIII-D tokamak demonstrate simultaneous access to high normalized density, current, pressure, and confinement. NT plasmas are shown to exist across an expansive parameter space compatible with high fusion power production, revealing surprisingly good core stability properties that compare favorably to conventional positive triangularity plasmas in DIII-D. Non-dimensionalizing the key parameters, expanded operating spaces featuring edge safety factors below 3, normalized betas above 3, Greenwald density fractions above 1, and high-confinement mode (H-mode) confinement qualities above 1 are observed, even simultaneously, and all with a robustly stable edge free from deleterious edge-localized mode instabilities. Scaling of the confinement time with engineering parameters reveals at least a linear dependence on plasma current although with significant power degradation, both in excess of expected H-mode scalings. These results increase confidence that NT plasmas are a viable approach to realize fusion power and open directions for future detailed study.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A machine learning approach to photometric metallicities of giant stars

ABSTRACT Despite the advances provided by large-scale photometric surveys, stellar features – such as metallicity – generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a neural network approach for estimating the metallicities and distances of red giant stars with 8-band photometry and parallaxes from Gaia EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties in the predictions arising from the range of possible outputs at each input and from the range of models compatible with the training set (through drop-out). A two-stage procedure is adopted where an initial network to estimate photoastrometric parallaxes is trained using a large sample of noisy parallax data from Gaia EDR3 and then a secondary network is trained using spectroscopic metallicities from the APOGEE and LAMOST surveys and an augmented feature space utilizing the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $\pm 0.19\, \mathrm{dex}$. The methodology is applied to stars within the Galactic bar/bulge with particular focus on a sample of 1.69 million objects with Gaia radial velocities. We demonstrate the use and validity of our approach by inspecting both spatial and kinematic gradients with metallicity in the Galactic bar/bulge recovering previous results on the vertical metallicity gradient (−0.528 ± 0.002 dex kpc−1) and the vertex deviation of the bar ($-21.29\pm 2.74\, \mathrm{deg}$).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Phase-resolved terahertz nanoimaging of W Te 2 microcrystals

The terahertz (THz) electrodynamics of few-layer W⁢Te 2 is dominated by the plasmon response. However, THz surface plasmons (SPs) with long wavelengths in two-dimensional exfoliated crystals are typically confined by the lateral geometry. Direct visualization of the plasmonic standing wave patterns is challenging due to the spatial confinement and low quality factor of the SP, especially for samples that are only a few monolayers thick. Here, we resolve subtle real-space features of the plasmonic response of W⁢Te 2 by augmenting more common scattering amplitude experiments with the phase contrast accomplished within the time-domain version of THz nanoimaging. Amplitude and phase images allow us to quantitatively evaluate the evolution of the plasmonic response at cryogenic temperatures in samples with variable thickness from 3 to 12 monolayers. Further, the proposed imaging modality is universally applicable to the THz near-field nanoscopy of low-dimensional materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Nuclear masses learned from a probabilistic neural network

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian neural networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN provides not only mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Coupling of magnetism, crystal lattice, and transport in EuCuP and EuCuAs

EuCuP and EuCuAs are members of a family of materials with candidates for realizing magnetically tuned electronic topology. Here, in this work, the magnetic phase transitions and magnetoelastic coupling of EuCuP and EuCuAs have been investigated. Around the Curie temperature, the thermal expansion coefficient and specific-heat capacity of EuCuP are found to have two closely spaced features, at approximately 30 and 31.3 K, which may indicate a cascading magnetic transition or perhaps an electronic transition closely coupled to the magnetic ordering. The zero-field magnetoelastic coupling appears to be stronger in EuCuP than in EuCuAs, and field-dependent measurements suggest this is due to the dominant ferromagnetic interaction in EuCuP as opposed to antiferromagnetic order in EuCuAs below T N =13.5 K. Thermal expansion is anisotropic around T N in EuCuAs, with a maximum expansion along [001] occurring above T N prior to a stiffening of this c -axis component on cooling through T N ; the expansion of the basal plane has a lambda-like peak centered slightly above T N . Matching these behaviors, the ac susceptibility data for EuCuAs suggest the presence of strong ferromagnetic correlations above T N in the vicinity where the c-axis expansion peaks. Both compounds possess similar electrical and thermoelectric transport behaviors, with short-range magnetic order likely playing an important role above T C or T N . Transport properties suggest these are predominantly hole doped due to intrinsic defects, and narrow-gap semiconducting or semimetallic behavior may be achievable if the underlying defects can be tuned.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding nanoscale structural distortions in Pb(Zr 0.2 Ti 0.8 )O 3 by utilizing X-ray nanodiffraction and clustering algorithm analysis

Hard X-ray nanodiffraction provides a unique nondestructive technique to quantify local strain and structural inhomogeneities at nanometer length scales. However, sample mosaicity and phase separation can result in a complex diffraction pattern that can make it challenging to quantify nanoscale structural distortions. In this work, a k-means clustering algorithm was utilized to identify local maxima of intensity by partitioning diffraction data in a three-dimensional feature space of detector coordinates and intensity. This technique has been applied to X-ray nanodiffraction measurements of a patterned ferroelectric PbZr 0.2 Ti 0.8 O 3 sample. The analysis reveals the presence of two phases in the sample with different lattice parameters. A highly heterogeneous distribution of lattice parameters with a variation of 0.02 Å was also observed within one ferroelectric domain. This approach provides a nanoscale survey of subtle structural distortions as well as phase separation in ferroelectric domains in a patterned sample.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Sheaves as a Framework for Understanding and Interpreting Model Fit

As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn from peripheral sources. In this paper we argue that in such situations, sheaves can provide a natural framework to analyze how well a statistical model fits at the local level (that is, on subsets of related datapoints) vs the global level (on all the data). The sheaf-based approach that we propose is suitably general enough to be useful in a range of applications, from analyzing sensor networks to understanding the feature space of a deep learning model.

Kvinge, Henry J.↗
DOE OSTI2022

Conceptual Design of a Scintillator-Based Fast-Ion Loss Detector for the Wendelstein 7-X Stellarator

Here, a conceptual design of a scintillator-based fast-ion loss detector (FILD) has been developed for the Wendelstein 7-X stellarator (W7-X). Simulations using the Monte Carlo codes ASCOT5 and BEAMS3D have been performed to calculate the expected flux of neutral beam injection (NBI)-generated fast hydrogen ions onto the conceptual detector probe head. These fast-ion loss fluxes have been calculated for several magnetic field configurations as well as probe insertion positions. At the maximum insertion position, both co-and counter-going losses with high incident pitch angles are observed; however, at retracted positions, only co-going fast ions reach the probe head. The FILDSIM code has been used to optimize the geometry of the detector entrance and collimating elements to achieve a wide velocity space coverage as well as a high velocity–space resolution. A synthetic FILD signal is calculated for the expected loss distribution via forward modeling using the instrument response function. The synthetic signal is found to largely retain the velocity space features of the loss distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Powertrain Autoresearch (TETA Autoresearch: RouteE Powertrain Experiments) [SWR-26-090]

Powertrain Autoresearch is an experiment repository based on the template set in teta-autoresearch containing branch history of LLM workers investigating powertrain ML model improvements. This repository contains an instance of teta-autoresearch applied to ML model exploration for RouteE Powertrain. An AI agent and human-in-the-loop iteratively explored feature space, ML model type, and hyperparameter spaces in an attempt to improve over existing models. The repo catalogs the result for review at USRSE 2026 Conference. Related: https://github.com/NatLabRockies/teta-autoresearch.

Reinicke, Nicholas [National Laboratory of the Roc↗
DOE OSTI2022

High-precision inversion of dynamic radiography using hydrodynamic features

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that using only features that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Full event particle-level unfolding with variable-length latent variational diffusion

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.

Shmakov, Alexander↗
DOE OSTI2021

Verification of Data-Driven Models of Physical Phenomena using Interpretable Approximation

Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.

42 ENGINEERING↗
DOE OSTI2023

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Mini Report: Jailbreaking Attacks and Defenses

Overall, jailbreaking defenses are unreliable, and no defenses proposed thus far can completely stop such attacks in any verifiable way. Even manual attacks can trivially bypass some claimed ‘defenses’, and over the course of 2023 automated attacks on prior models have been adapted to work on LLMs while other attacks draw on ideas such as fuzz testing. At best, some defenses can make jailbreaks more difficult, but with the developing landscape of attacks existing papers have not robustly evaluated how effective they are against all these methods. However, our opinion is that given the way these large generative models are trained and ‘aligned’ to stated goals of safety via fine tuning, it will be exceedingly difficult if not impossible to eliminate the possibility of jailbreaking attacks. A major barrier is that the feature space of LLMs is not sufficiently understood in a way where guarantees can be made about the outputs. Barring major changes, the expectation around jailbreaking defenses should be that they can mitigate misuse, but not verifiably prevent it. However, one defense we believe merits further investigation depends on the fact as automated attacks produce text, they need an automated way to identify a successful jailbreak - a judgment model. We have seen some attempts to repurpose models like these to defend against jailbreaks, but the evaluations are small scale and not robust.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗
DOE OSTI2021

Machine Learning Using Rapidity-Mass Matrices for Event Classification Problems in HEP

In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM) inputs are studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the Large Hadron Collider (LHC) when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in the search for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗