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DOE OSTI2025

A Class of Sparse Johnson–Lindenstrauss Transforms and Analysis of their Extreme Singular Values

The Johnson–Lindenstrauss (JL) lemma is a powerful tool for dimensionality reduction in modern algorithm design. The lemma states that any set of high-dimensional points in a Euclidean space can be projected into lower dimensions while approximately preserving pairwise Euclidean distances. Random matrices satisfying this lemma are called JL transforms (JLTs). Inspired by existing $s$-hashing JLTs with exactly $s$ nonzero elements on each column, the present work introduces an ensemble of sparse matrices encompassing so-called $s$-hashing-like matrices whose expected number of nonzero elements on each column is $s$. The independence of the sub-Gaussian entries of these matrices and the knowledge of their exact distribution play an important role in their analyses. Using properties of independent sub-Gaussian random variables, these matrices are demonstrated to be JLTs, and their smallest nontrivial singular values and largest singular values are estimated nonasymptotically using a technique from geometric functional analysis. As the dimensions of the matrix grow to infinity, these singular values are proved to converge almost surely to fixed quantities (by using the universal Bai–Yin law) and in distribution to the Gaussian orthogonal ensemble Tracy–Widom law after proper rescalings. Understanding the behaviors of extreme singular values is important in general because they are often used to define a measure of stability of matrix algorithms. For example, JLTs were recently used in derivative-free optimization algorithmic frameworks to select random subspaces in which are constructed random models or poll directions to achieve scalability, and hence estimating their smallest singular value in particular helps determine the dimension of these subspaces.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Kinklike structures in an arcsin real scalar dynamics

Highlights: • Kinklike analyses in a real scalar theory with an arcsin dynamic. • First-order formalism implementation and deformation procedure in an arcsin scalar dynamics. In this paper, we analyze kink-like analytical solutions in a real scalar theory with an arcsin dynamics inspired by the arcsin electrodynamics presented in Kruglov (2015). This analysis is done by means of the first-order formalism. This formalism provides a framework where the equations of motion can be simplified by preserving the linear stability of the theory. In this work, the deformation procedure is implemented with the aim of finding exact solutions in systems with generalized dynamics. Along the paper, we explore how the first-order formalism is implemented in the arcsin kinetics and how such a term influences the kink-like solutions. As a part of the result of our paper, we show that the kink-like solutions are similar to the ones obtained in the standard scalar kinetic theory. We also show that the extra parameter, that controls the non-linearities of the model, plays an essential role in the energy densities and stability potentials. These quantities vary according to this parameter. The goals here are to show how the first-order framework is implemented in this arcsin scenario and to present the analytical kink-like solutions that can be found by means of the first-order framework and deformation method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

A finite difference informed random walker (FDiRW) solver for strongly inhomogeneous diffusion problems

In nature, many complex multi-physics coupling problems exhibit strong diffusivity inhomogeneity. For instance, in the context of radionuclide absorption by porous wasteform materials within a flowing waste stream, the difference of species’ diffusivity in solid and liquid phases spans by 3~8 orders of magnitude. To solve the diffusion equations with strongly inhomogeneous diffusivity, traditional discretization-based methods, such as the Finite Difference Method (FDM), require infinitesimally small time steps (<10 -10 ) as high spatial resolutions are employed in most microstructure evolution processes, leading to prohibitively high computational costs. Here, this work developed an integrated numerical approach (FDiRW: Finite Difference informed Random Walk) to tackle this challenge. The idea is that utilizing the Random Walk concept, the fast diffusion is modeled as a superposition of point source’s solution for a concentration distribution while FDM is used to obtain the point source’s solution at each node. A mesh-coarsening algorithm is developed to generate an exclusive coarse mesh for FDiRW approach to maximize its efficiency. The effectiveness of the coarse mesh-based FDiRW approach is validated by benchmarking Finite Difference solutions. Numerical results demonstrated that FDiRW achieves a remarkable 1000x computational efficiency improvement over FDM while preserving desired accuracy for a medium-sized model of 192 × 192 × 192 grids. Finally, as models scale up, a floating-point operations (PLOPs) analysis of the FDiRW algorithm reveals that its computational complexity grows quadratically in terms of the number of nodes employed in computation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

DNA origami single crystals with Wulff shapes

DNA origami technology has proven to be an excellent tool for precisely manipulating molecules and colloidal elements in a three-dimensional manner. However, fabrication of single crystals with well-defined facets from highly programmable, complex DNA origami units is a great challenge. Here, we report the successful fabrication of DNA origami single crystals with Wulff shapes and high yield. By regulating the symmetries and binding modes of the DNA origami building blocks, the crystalline shapes can be designed and well-controlled. The single crystals are then used to induce precise growth of an ultrathin layer of silica on the edges, resulting in mechanically reinforced silica-DNA hybrid structures that preserve the details of the single crystals without distortion. The silica-infused microcrystals can be directly observed in the dry state, which allows meticulous analysis of the crystal facets and tomographic 3D reconstruction of the single crystals by high-resolution electron microscopy.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Impact of charge-density-wave pattern on the superconducting gap in Vanadium-based kagome superconductors

Kagome metals AV 3 Sb 5 (A = K, Rb, Cs) provide a compelling platform to explore the interplay between superconductivity (SC) and charge-density-wave (CDW) orders. While distinct CDW orders have been identified in K/RbV 3 Sb 5 versus CsV 3 Sb 5 , their influence on the SC order parameter remains unresolved. Here, we investigate low-energy quasiparticle excitations in AV 3 Sb 5 , uncovering a striking difference in SC gap anisotropy: K/RbV 3 Sb 5 exhibit fully gapped, nearly isotropic s-wave states, in contrast to the strongly anisotropic SC gap in CsV 3 Sb 5 . Impurity scattering introduced via electron irradiation in K/RbV 3 Sb 5 has a minimal impact on low-energy excitations, and it induces an increase in the SC transition temperature T c , consistent with more isotropic s-wave SC competing with CDW order. Our theoretical analysis attributes the observed SC gap anisotropy differences to distinct CDW modulation patterns: the star-of-David structure unique to CsV 3 Sb 5 preserves van Hove singularities near the Fermi level, promoting anisotropic s-wave SC with enhanced T c via bond-order fluctuations. These findings establish a systematic framework for understanding the interplay between SC and CDW orders in AV 3 Sb 5 , driven by electron correlations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

A high-solid DES pretreatment using never-dried biomass as the starting material: towards high-quality lignin fractionation

This study investigated a high-solid diol deep eutectic solvent (DES) pretreatment using a wet substrate as the starting material. This pretreatment led to a remarkable glucan saccharification of 94.8% with efficient lignin and xylan removal (as high as 63.1% and 73.0%, respectively). Here the chemical structures of the substrates were analyzed comprehensively to reveal the impact of the pretreatment. In addition, over 90% of the removed lignin was recovered from the pretreated liquid, which exhibited a well-preserved β-O-4 structure (46–56/100Ar). The protection mechanism of our DES was investigated by 2D HSQC NMR, GPC, and 31 P NMR analysis. This study emphasized that diol-based DES pretreatment of undried lignocellulosic biomass at a high-solid loading can significantly utilize both carbohydrates and lignin fractions with high saccharification yields and high-quality lignin as a co-product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Characterizing Shielded Special Nuclear Material by Neutron Capture Gamma-Ray Multiplicity Counting

We present a new neutron multiplicity counting analysis and measurement method for neutron-shielded fissile material using neutron-capture gamma rays. Neutrons absorbed in shielding produce characteristic gamma rays that preserve the otherwise lost neutron multiplicity signature. Neutron multiplicity counting provides estimates of fission parameters, such as neutron leakage multiplication, spontaneous fissioner (e.g. Pu-240) mass, and (α,n) ratio. Standard neutron multiplicity counting can incorporate the new neutron-capture gamma-ray multiplicity counting technique to characterize previously degenerate or intractable source configurations by maximizing the multiplicity signature. The new method decouples neutron source-detector interferences, such as reflection and thermalization time in the detector, that could improve measurements of the mean neutron lifetime. We also develop a detector prototype for the multiplicity counting of neutron-capture gamma rays and present detector design considerations, such as detection material and shielding, to optimize the detection of the 2.2 MeV hydrogen capture gamma ray. We simulate the prototype neutron- capture gamma-ray multiplicity counter against the BeRP ball in polyethylene shells to inform future measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]

This report includes a compilation of several slide presentations: 1) Interatomic Potentials for Materials Science and Beyond–Advances in Machine Learned Spectral Neighborhood Analysis Potentials (Wood); 2) Agile Materials Science and Advanced Manufacturing through AI/ML (de Oca Zapiain); 3) Machine Learning for DFT Calculations (Rajamanickam); 4) Structure-preserving ML discovery of a quantum-to-continuum codesign stack (Trask); and 5) IBM Overview of Accelerated Discovery Technology (Pitera)

36 MATERIALS SCIENCE↗

DOE OSTI2022

Updates to the n+ 63,65 Cu Angular Distributions for Critical Experiments [Slides]

This work updates the evaluation of n+ 63,65 Cu for neutron energies in the resolved resonance region (RRR) and slightly higher energies. The updates strive to improve the statistical properties of the resonance parameters and the continuity of the evaluation between the RRR and the high-energy region while preserving or improving critical assembly benchmark performance. We will review our work on the resonance parameters and present the current status of our angular distribution analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Updates to the n+ 63,65 Cu Evaluations: RRR and Angular Distributions [Slides]

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1997

Fossilization of Coniform (Phormidium) Stromatolites In Siliceous Thermal Springs, Yellowstone National Park

We have studied fossilization processes in well-developed cyanobacterial mats present in mid-terrace ponds of silica-precipitating thermal springs of Yellowstone National Park. These mats occur over temperatures of approx.35-60 C and are dominated by species of Phormidium. Within Bonded systems two end member environments have been distinguished based temperature, depth, and the stability of spring inflows, each differing substantially in the style of stromatolite morphogenesis and fossilization. Type 1 systems include shallow, ephemeral ponds where water flow and temperature fluctuates widely on a seasonal basis; mats typically secrete rapidly up to the air-water interface, forming exposed islands. Silicification occurs primarily by the wicking of water onto the surface of exposed mats, leading to the evaporative concentration of silica near the surface. pH microelectronic measurements in partially-exposed columns show strong gradients in pH with values exceeding 10.5 in the upper am of silicifying mats, decreasing to <9.0 a few mm below the surface. High oxygen concentrations lead to the rapid oxidation of most organic materials. In Type I systems, the tops of coniform and columnar stromatolites become silicified first, followed by the bases. This typically leads to extensive fragmentation during the initial stages of burial. Case 2 systems include deeper ponds where the water flow, temperature and depth are seasonally more stable, and where mats develop larger-scale tufts and columns. Alkalinity in permanently submerged mats increases into tuft interiors from approx.9.0 near the surface to >10.0 at depth. Moderate silicification is apparent throughout mat frameworks, although there is frequently a densely silicified core near the base. In Type 2 systems, preservation of the coniform and columnar architecture of stromatoilites is much more robust. Sub-fossil examples suggest minimal fragmentation prior to burial. Comparative rapid analysis of the phone zones of submerged and exposed (silicifying) mats demonstrated the recovery of comparable levels of esterified fatty acids in both types of mats. Membrane lipids apparently remain intact during the early stages of silicification, although their fate during early diagenesis is still being evaluated.

Farmer, Jack↗

NASA NTRS2017

Nonlinear Large Deflection Theory with Modified Aeroelastic Lifting Line Aerodynamics for a High Aspect Ratio Flexible Wing

This paper investigates the effect of nonlinear large deflection bending on the aerodynamic performance of a high aspect ratio flexible wing. A set of nonlinear static aeroelastic equations are derived for the large bending deflection of a high aspect ratio wing structure. An analysis is conducted to compare the nonlinear bending theory with the linear bending theory. The results show that the nonlinear bending theory is length-preserving whereas the linear bending theory causes a non-physical effect of lengthening the wing structure under the no axial load condition. A modified lifting line theory is developed to compute the lift and drag coefficients of a wing structure undergoing a large bending deflection. The lift and drag coefficients are more accurately estimated by the nonlinear bending theory due to its length-preserving property. The nonlinear bending theory yields lower lift and span efficiency than the linear bending theory. A coupled aerodynamic-nonlinear finite element model is developed to implement the nonlinear bending theory for a Common Research Model (CRM) flexible wing wind tunnel model to be tested in the University of Washington Aeronautical Laboratory (UWAL). The structural stiffness of the model is designed to give about 10% wing tip deflection which is large enough that could cause the nonlinear deflection effect to become significant. The computational results show that the nonlinear bending theory yields slightly less lift than the linear bending theory for this wind tunnel model. As a result, the linear bending theory is deemed adequate for the CRM wind tunnel model.

Aeroelasticity↗

DOE OSTI2023

The Postdoc Accord in Theoretical High Energy Physics

We present the results of a survey meant to assess the opinion of the high-energy physics theory (HET) community on the January 7th postdoc acceptance deadline - specifically, whether there is a preference to shift the deadline to later in January or February. This survey, which served for information-gathering purpose only, is part of a community conversation on the optimal timing of an acceptance deadline and whether the community would be better served by a later date. In addition, we present an analysis of data from the postdoc Rumor Mill, which gives a picture of the current hiring landscape in the field. We emphasize the importance of preserving a universal deadline, and the current results of our survey show broad support for a shift to a later date. A link to the survey, frequently asked questions, a running list of supporters, and next steps can be found on our companion web page.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗

NASA NTRS2004

Rapid recovery from the Late Ordovician mass extinction

Understanding the evolutionary role of mass extinctions requires detailed knowledge of postextinction recoveries. However, most models of recovery hinge on a direct reading of the fossil record, and several recent studies have suggested that the fossil record is especially incomplete for recovery intervals immediately after mass extinctions. Here, we analyze a database of genus occurrences for the paleocontinent of Laurentia to determine the effects of regional processes on recovery and the effects of variations in preservation and sampling intensity on perceived diversity trends and taxonomic rates during the Late Ordovician mass extinction and Early Silurian recovery. After accounting for variation in sampling intensity, we find that marine benthic diversity in Laurentia recovered to preextinction levels within 5 million years, which is nearly 15 million years sooner than suggested by global compilations. The rapid turnover in Laurentia suggests that processes such as immigration may have been particularly important in the recovery of regional ecosystems from environmental perturbations. However, additional regional studies and a global analysis of the Late Ordovician mass extinction that accounts for variations in sampling intensity are necessary to confirm this pattern. Because the record of Phanerozoic mass extinctions and postextinction recoveries may be compromised by variations in preservation and sampling intensity, all should be reevaluated with sampling-standardized analyses if the evolutionary role of mass extinctions is to be fully understood.

Evolution↗

DOE OSTI2025

S AP F LOWER : an automated tool for sap flow data preprocessing, gap-filling, and analysis using deep learning

Sap flow, a critical process in plant water use and ecosystem water cycles, is often measured using thermal dissipation probes (TDP) due to their ease of installation and continuous data collection. However, sap flow data frequently include noise, outliers, and gaps, creating challenges for analysis and requiring substantial manual processing. We developed S AP F LOWER , a tool that automates data preprocessing, model training, gap-filling, sapwood area scaling and modeling, and water use analysis. It integrates autocleaning, machine learning and deep learning models (e.g. random forest, Gaussian process regression, long short-term memory (LSTM), bidirectional LSTM (BiLSTM)), and efficient workflows to process sap flow data. S AP F LOWER can remove over 90% of noisy data while preserving legitimate variations and achieve high accuracy in gap-filling based on user-determined parameters. Random forest, LSTM, and BiLSTM models reduced root mean square error to 10% or less for long-term gaps. Model training and prediction can be performed efficiently within seconds. S AP F LOWER significantly enhances the efficiency and accessibility of TDP data analysis by automating complex tasks, enabling researchers without programming expertise to employ advanced techniques. Future improvements will focus on species-specific corrections for TDP and support for additional measurement methods. S AP F LOWER is openly available on GitHub (https://github.com/JiaxinWang123/SapFlower) and Zenodo (doi: 10.5281/zenodo.13665919).

ecosystem water balance↗

DOE OSTI2024

Electrically interfaced Brillouin-active waveguide for microwave photonic measurements

New strategies for converting signals between optical and microwave domains could play a pivotal role in advancing both classical and quantum technologies. Traditional approaches to optical-to-microwave transduction typically perturb or destroy the information encoded on intensity of the light field, eliminating the possibility for further processing or distribution of these signals. In this paper, we introduce an optical-to-microwave conversion method that allows for both detection and spectral analysis of microwave photonic signals without degradation of their information content. This functionality is demonstrated using an optomechanical waveguide integrated with a piezoelectric transducer. Efficient electromechanical and optomechanical coupling within this system permits bidirectional optical-to-microwave conversion with a quantum efficiency of up to -54.16 dB. Leveraging the preservation of the optical field envelope in intramodal Brillouin scattering, we demonstrate a multi-channel microwave photonic filter by transmitting an optical signal through a series of electro-optomechanical waveguide segments, each with distinct resonance frequencies. Such electro-optomechanical systems could offer flexible strategies for remote sensing, channelization, and spectrum analysis in microwave photonics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Block-Structured Operator Inference for Coupled Multiphysics Model Reduction

This work presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from snapshot data following the nonintrusive Operator Inference methodology. In addition to preserving physical system structure, which in turn permits preservation of system properties such as stability and second-order structure, the block-structured approach has the advantages of reducing the overall dimensionality of the learning problem and admitting tailored regularization for each physics component. The numerical advantages of the block-structured formulation over a monolithic Operator Inference formulation are demonstrated for aeroelastic analysis, which couples aerodynamic and structural models. For the benchmark test case of the AGARD 445.6 wing, block-structured Operator Inference provides an average 20% online prediction speedup over monolithic Operator Inference across subsonic and supersonic flow conditions in both the stable and fluttering parameter regimes while preserving the accuracy achieved with monolithic Operator Inference.

42 ENGINEERING↗