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DOE OSTI2026

Cyote Insights

CyOTE Insights leverages React, Vite, Typescript, Tailwind, and Daisy UI for the Graphical User Interface. It was designed in a particular style with a dark mode and a light mode. All code is broken down into components and reusable wrapper components for efficiency. All data is stored in Deep Lynx as a central data repository using an ontology based schema. The application serves as a main endpoint for the data in the COREII and CyOTE programs. The main purpose of the application is to display historical attack data in the Operational Technology space. At the time of this writing, it supports 27 historical attack reports compiled from OSINT sources. All of the data is publicly available, but what this application offers is the ability to see many years worth of publications in a detailed dashboard. It will also support future reports that are written using the other applications in the COREII program.

Pluth, AdamJ [Idaho National Laboratory (INL), Ida↗

DOE OSTI2021

Conversion efficiency of soliton Kerr combs

We investigate the conversion efficiency (CE) of soliton modelocked Kerr frequency combs. Our analysis reveals three distinct scaling regimes of CE with the cavity free spectral range (FSR), which depends on the relative contributions of the coupling and propagation loss to the total cavity loss. Our measurements, for the case of critical coupling, verify our theoretical prediction over a range of FSRs and pump powers. Our numerical simulations also indicate that mode crossings have an adverse effect on the achievable CE. Our results indicate that microresonator combs operating with spacings in the electronically detectable regime are highly inefficient, which could have implications for integrated Kerr comb devices.

Jang, Jae K. (ORCID:0000000187786058)↗

DOE OSTI2022

Zero modes and matching for the twist-3 PDFs

The quasi-PDF approach, proposed by X. Ji in 2013, has made it possible to directly extract light-cone PDFs from lattice QCD. This approach relies on the extraction of matrix elements of space-like operators for fast-moving hadrons. Quasi-PDFs can be related to the light-cone PDFs through a perturbatively calculable matching coefficient. We explore the formalism of matching, for the very first time, for the twist-3 PDFs g_{T}(x) g T ( x ) , e(x) e ( x ) and h_{L}(x) h L ( x ) . In this work, we address the non-trivialities involved in the extraction of the matching coefficient due to the presence of (singular) zero-mode contributions.

Bhattacharya, Shohini↗

DOE OSTI2020

Nonproliferation Legacy Technology and Artifact Collection: NASA Space Shuttle Tiles

The NASA Space Shuttle Tiles were used by Sandia in the process of developing the Laser Dynamic Range Imager (LDRI) in support of NASA's Return to Flight following the 2003 Space Shuttle Columbia disaster. The heat shield tiles, provided to Sandia by NASA, are identical to those that were located on the underbelly of the Space Shuttle Columbia's orbiter. Sandia used the tiles to test the efficacy of the LDRI's imaging capabilities. The LDRI was utilized during every space shuttle mission between 2005 and 2011. The tiles are currently located in Building 891 and need to be moved to free up space for operational use. Given their technical significance, Sandia would like to archive them as historically significant items in long-term storage until such time as they can be appropriately displayed or employed as a demonstration artifact. This document provides basic information about the provenance of this artifact.

42 ENGINEERING↗

DOE OSTI2026

Editorial: Predicting near-earth space environment: new perspective and capabilities in the AI age

Editorial on the Research Topic Predicting near-earth space environment: new perspective and capabilities in the AI age The near-Earth space environment is not only an operational hazard for space missions, but also a scientific laboratory for advancing our understanding and prediction of space plasma populations. This Research Topic is organized around three interconnected themes: observational datasets, machine-learning (ML) model development, and the discovery of new physical insights through those models. Its primary goal is to highlight the emerging capabilities in space environment prediction that are enabled, or will be enabled, by integrating advanced techniques—including AI/ML methods—with long-term curated datasets.

58 GEOSCIENCES↗

DOE OSTI2022

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1°; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDA norm (z) considering the differences between the instruments. We define CDA norm (z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDA$_{norm}^{theor}$(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ~78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ~3 km height. (b) The CF(z) profiles show agreement below ~3 km, where ~80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDA$_{norm}^{obs}$(z) reduces to ~50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ~0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDA$_{norm}^{obs}$(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDA$_{norm}^{obs}$(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Latent-Space Dynamics for Prediction and Fault Detection in Geothermal Power Plant Operations

This paper presents a latent-space dynamic neural network (LSDNN) model for the multi-step-ahead prediction and fault detection of a geothermal power plant’s operation. The model was trained to learn the dynamics of the power generation process from multivariate time-series data and the effects of exogenous variables, such as control adjustment and ambient temperature. In the LSDNN model, an encoder–decoder architecture was designed to capture cross-correlation among different measured variables. In addition, a latent space dynamic structure was proposed to propagate the dynamics in the latent space to enable prediction. The prediction power of the LSDNN was utilized for monitoring a geothermal power plant and detecting abnormal events. The model was integrated with principal component analysis (PCA)-based process monitoring techniques to develop a fault-detection procedure. The performance of the proposed LSDNN model and fault detection approach was demonstrated using field data collected from a geothermal power plant.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network

Abstract A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the operational stability boundary of tokamak plasma discharges. This model was implemented using a dataset of over 3000 discharges from the high beta tokamak-extended pulse (HBT-EP) device. We found the VAE model to be capable of forming a continuous low-dimensional operational space map and identifying the operational boundaries using a specified warning time window. By projecting the operational parameters onto the same reduced space, this provides an intuitive way for the machine operator or an automated control system to perform disruption avoidance using a relevant control actuator as a discharge approaches a boundary. Pre-programmed GPU control experiments were conducted to demonstrate this control technique using HBT-EP’s saddle control coils as a horizontal position actuator, showing the ability to avoid the oncoming disruptive event and extend the duration of the discharge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Evaluating Variable-Impedance Magnetically-Insulated Transmission Lines as a Risk-Mitigation Measure for Next-Generation Pulsed Power

This project has produced the first detailed characterizations of power flow resulting from applying the “variable-impedance MITL” concept to real-life systems in Sandia’s pulsed power program (Z and next-generation pulsed power (NGPP)). We present simulation results and analyses for constant-impedance versions of both Z and NGPP and survey the operational viability of several variable-impedance re-designs in the parameter space of linear tapers. Circuit modeling (SCREAMER/Bertha) was used to pinpoint promising candidate designs, and EM-PIC (Empire) simulations were used to evaluate these candidates more rigorously. This approach was particularly successful in the Z regime which resulted in the identification of several viable variable-impedance MITL designs for each level. The approach was more challenged in the operating space NGPP occupies, producing data points that speak to a more restrictive design space due to anode plasma turn-on. In the end, we were able to converge on one viable variable-impedance design for the highest inductance line (level “F”) and one for the highest current line (level “A”). Altogether, the body of simulation evidence presented in this report suggest there does exist flexibility in operating space for magnetically-insulated transmission lines (MITLs) having variable geometric impedance to be a potential enabling technology for safely increasing current delivery (and potentially lowering stack voltage) in pulsed-power drivers by manipulating electron losses; however, operating points for a particular design must be carefully screened. Circuit and EM-PIC modeling provided consistent verdicts in safe operating regimes for operational viability, but additional physics such as anode plasma turn-on which is included in Empire but not in SCREAMER/Bertha was found to be a critical factor affecting power flow that lead to different assessments between the codes. It is not always the case that the occurrence of anode plasma caused a design to fail (some designs turned on anode plasma yet still delivered load currents meeting design targets); the details matter such as how early in the pulse anode surfaces break down (and how large a region). However, in every case that it did fail it was found that the feedback from anode plasma was the cause (i.e., turning off the anode plasma model in Empire restored agreement with the circuit model prediction). As circuit simulations represent an efficient and practical means of surveying design space compared to more computationally-expensive approaches such as EM-PIC, it could be prudent to invest in the research and development of models to include the effects of anode plasma such as ion emission in circuit codes. The variable-impedance MITL design is a new concept that enables controlled manipulation of the initial electron losses in the outer MITL and can be tested on Z today. We encourage follow-on work to explore further optimization (including alternative variable-impedance profiles, e.g., having constant dZ/dR), and to confirm the major findings presented in this report by fielding test hardware on actual Z shots.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Multigrid Algorithms with Projection and Prolongation over Elements of the Phase Space for K-Eigenvalue Transport Problems

This paper describes new multilevel acceleration methods for solving the multigroup neu- tron transport eigenvalue problems. These multilevel algorithms use different projection and prolongation operators in the phase space. The Nonlinear Diffusion Acceleration (NDA) method with multiple grids in energy is formulated with the prolongation oper- ator based on multiplication iterative correction and linear-in-energy mapping. Another multilevel NDA method uses the projection operator with coarsening in energy between the high-order transport and low-order NDA equations. The third algorithm is formu- lated with the partial-current based CMFD low-order equations and applies projection operators in space and energy. The numerical results are presented.

Cornejo, Luke↗

DOE OSTI2022

Understanding the fundamental driver of semiconductor radiation tolerance with experiment and theory

Space is the operating environment of a multitude of systems that our society is heavily reliant on today, however, maintaining operability there necessitates special consideration of the electronic systems' tolerance of space radiation. Electronic systems are critically dependent on the electronic properties of their semiconductor components, which are modified by space radiation with an adverse impact on the space system performance. What innate property allows some semiconductors to sustain little damage while others accumulate defects rapidly with dose is poorly understood, which limits the extent to which radiation tolerance can be implemented as a design criterion. Here, to gain insight into what properties are drivers of semiconductor radiation tolerance, the first step is to generate a dataset of the relative radiation tolerance of a broad sampling of semiconductors. To accomplish this, Rutherford backscatter channeling experiments are used to compare the displaced lattice atom buildup in InAs, InP, GaP, GaN, ZnO, MgO, and Si as a function of stepwise alpha particle dose. With this experimental information on radiation-induced incorporation of interstitial defects in hand, hybrid density functional theory electron densities (and their derived quantities) are calculated and their gradient and Laplacian are evaluated to obtain key fundamental information about the interactions in each material. It is shown that simple, undifferentiated values (which are typically used to describe bond strength) are insufficient to predict radiation tolerance. Instead, the curvature of the electron density at bond critical points provides a measure of radiation tolerance consistent with the experimental results obtained. This curvature and associated forces surrounding bond critical points have the potential to disfavor the localization of displaced lattice atoms at these points, favoring their diffusion toward perfect lattice positions. With this criterion to predict radiation tolerance, simple density functional theory simulations can be conducted on potential new materials to gain insight into how they may operate in demanding high radiation environments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗

DOE OSTI2020

Bilevel parameter optimization for learning nonlocal image denoising models

We propose a bilevel optimization approach for the estimation of parameters in nonlocal image denoising models. The parameters we consider are both the space-dependent fidelity weight and weights within the kernel of the nonlocal operator. In both cases we investigate the differentiability of the solution operator in function spaces and derive a first order optimality system that characterizes local minima. For the numerical solution of the problems, we propose a second-order trust-region algorithm in combination with a finite element discretization of the nonlocal denoising models and we introduce a computational strategy for the solution of the resulting dense linear systems. Several experiments illustrate the applicability and effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Operator origin of anomalous dimensions in de Sitter space

The late-time limit of the power spectrum for heavy (principal series) fields in de Sitter (dS) space yields a series of polynomial terms with complex scaling dimensions. Such scaling behavior is expected to result from an associated operator with a complex dimension. In a free theory, these complex dimensions are known to match the constraints imposed by unitarity on the space of states. Yet, perturbative corrections to the scaling behavior of operators are naively inconsistent with unitary evolution of the quantum fields in dS space. This paper demonstrates how to compute one-loop corrections to the scaling dimensions that appear in the two-point function from the field theory description in terms of local operators. We first show how to evaluate these anomalous dimensions using Mellin space, which has the feature that it naturally accommodates a scaleless regulator. We then explore the consequences for the soft de Sitter effective theory (SdSET) description that emerges in the long wavelength limit. Carefully matching between the UV and SdSET descriptions requires the introduction of novel nondynamical “operators” in the effective theory. This is not only necessary to reproduce results extracted from the Källén-Lehmann representation (that use the space of unitary states directly), but it is also required by general arguments that invoke positivity. Published by the American Physical Society 2025

Cohen, Timothy↗

DOE OSTI2023

Semiclassical theory and the Koopman-van Hove equation *

Abstract The phase space Koopman-van Hove (KvH) equation can be derived from the asymptotic semiclassical analysis of partial differential equations. Semiclassical theory yields the Hamilton–Jacobi equation for the complex phase factor and the transport equation for the amplitude. These two equations can be combined to form a nonlinear semiclassical version of the KvH equation in configuration space. There is a natural injection of configuration space solutions into phase space and a natural projection of phase space solutions onto configuration space. Hence, every solution of the configuration space KvH equation satisfies both the semiclassical phase space KvH equation and the Hamilton–Jacobi constraint. For configuration space solutions, this constraint resolves the paradox that there are two different conserved densities in phase space. For integrable systems, the KvH spectrum is the Cartesian product of a classical and a semiclassical spectrum. If the classical spectrum is eliminated, then, with the correct choice of Jeffreys–Wentzel–Kramers–Brillouin (JWKB) matching conditions, the semiclassical spectrum satisfies the Einstein–Brillouin–Keller quantization conditions which include the correction due to the Maslov index. However, semiclassical analysis uses different choices for boundary conditions, continuity requirements, and the domain of definition. For example, use of the complex JWKB method allows for the treatment of tunneling through the complexification of phase space. Finally, although KvH wavefunctions include the possibility of interference effects, interference is not observable when all observables are approximated as local operators on phase space. Observing interference effects requires consideration of nonlocal operations, e.g. through higher orders in the asymptotic theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Space environment considerations for perovskite solar cell operations: A review

Designing new technology for extraterrestrial applications certainly presents unique challenges. The environmental stressors perovskite-based photovoltaics must overcome will vary with the environment in which they are deployed. One must consider mission requirements when designing photovoltaic devices and packaging. Different space "theaters" can have dramatically different stressors needing consideration for designing panels for solar power generation. Here, in this article, we review the relevant space environmental conditions that must be considered when designing perovskite-based photovoltaic devices for implementation in space. We specifically consider thermal, radiation, gaseous, weather, and other phenomena most relevant to photovoltaic operation for specific theaters such as Low Earth Orbit, Geosynchronous Orbit, Lunar surface, Mars (orbit and surface), and interplanetary exploration pathways.

14 SOLAR ENERGY↗

DOE OSTI2024

A collision operator for describing dissipation in noncanonical phase space

The phase space of a noncanonical Hamiltonian system is partially inaccessible due to dynamical constraints (Casimir invariants) arising from the kernel of the Poisson tensor. When an ensemble of noncanonical Hamiltonian systems is allowed to interact, dissipative processes eventually break the phase space constraints, resulting in a thermodynamic equilibrium described by a Maxwell–Boltzmann distribution. However, the time scale required to reach Maxwell–Boltzmann statistics is often much longer than the time scale over which a given system achieves a state of thermal equilibrium. Examples include diffusion in rigid mechanical systems, as well as collisionless relaxation in magnetized plasmas and stellar systems, where the interval between binary Coulomb or gravitational collisions can be longer than the time scale over which stable structures are self-organized. Here, we focus on self-organizing phenomena over spacetime scales such that particle interactions respect the noncanonical Hamiltonian structure, but yet act to create a state of thermodynamic equilibrium. We derive a collision operator for general noncanonical Hamiltonian systems, applicable to fast, localized interactions. This collision operator depends on the interaction exchanged by colliding particles and on the Poisson tensor encoding the noncanonical phase space structure, is consistent with entropy growth and conservation of particle number and energy, preserves the interior Casimir invariants, reduces to the Landau collision operator in the limit of grazing binary Coulomb collisions in canonical phase space, and exhibits a metriplectic structure. We further show how thermodynamic equilibria depart from Maxwell–Boltzmann statistics due to the noncanonical phase space structure, and how self-organization and collisionless relaxation in magnetized plasmas and stellar systems can be described through the derived collision operator.

Boltzmann equation↗