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DOE OSTI2025

Compositing and Characterization of SE Quadrant Waste Exemplars

This report outlines the experimental investigation and characterization of transport properties in Hanford SE quadrant High-Level Waste (HLW). The goal of the study was to establish baseline behaviors of bulk composite rheology and settling characteristics to facilitate waste treatment process design for the Waste Treatment and Immobilization Plant (WTP) and avoid waste conditions and properties favorable to bubble cascade gas release events. The study focused on two major objectives: 1) identifying, obtaining, and preparing relevant Hanford tank waste samples for evaluation and 2) quantifying the “as-received” rheology and transport properties of the samples. Twenty-three centrifuged core segments originating from tanks AN-101, AN-106, and AW-105 were selected based on compositional relevance to SE quadrant PUREX cladding waste. These materials were composited into five waste composites enriched with target analytes: aluminum (Al), iron (Fe), phosphate (PO 4 ), uranium (U), and zirconium (Zr). Physical property and transport testing examined particle size distributions, bulk densities, settling behaviors, rheological properties, shear strengths, and just-suspended mixing speeds (NJS). Testing revealed two distinct composite classifications based on rheological characteristics: non-Newtonian composites (Fe and PO 4 ) and Newtonian composites (Al, U, and Zr). The Fe and PO 4 composites exhibited slow settling rates and reduced mobilization proclivity, attributable to strong particle-particle interactions and the formation of yield structures within non-Newtonian slurries. In contrast, the Al, U, and Zr composites displayed rapid settling and dense compaction behaviors, indicative of minimal structuring and interactions. Shear strengths for all composites were generally low relative to prior studies of SE quadrant waste, with only the U composite showing elevated strength approaching values reported in previous literature. Repeat shear strength measurements revealed contributions from dense granular material in the U composite and stronger cohesive properties in the Al composite. Settling data highlighted hindered settling behavior, with rates falling more than one order of magnitude below estimates based on Stokes’ law and rate decreasing as composite UDS content increased. NJS testing demonstrated different mobilization behaviors between cohesive and granular composites. The Fe composite required the highest mixing rate for resuspension, while the Al composite was the easiest to resuspend. Comparison of measured NJS against predictions made using the Zwietering correlation suggests non-Newtonian behavior alters resuspension mechanics, rendering non-Newtonian systems more stable against resuspension lift forces relative to their granular counterparts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗

DOE OSTI2026

Evidence of Spin-Interference Effects in Exclusive 𝐽/𝜓 → 𝑒 + ⁢𝑒 − Photoproduction in Ultraperipheral Heavy-Ion Collisions

Here, we report the first evidence of spin interference in exclusive 𝐽/𝜓 → 𝑒 + ⁢𝑒 − photoproduction in ultraperipheral heavy-ion collisions at STAR at $\sqrt{𝑠_{𝑁⁢𝑁}}$ = 200 GeV. In Au + Au collisions, a negative cos⁡(2⁢𝜙) modulation is found for 𝑝 𝑇 < 120 MeV/𝑐 with a significance of 3.2⁢𝜎, while the isobar data (Ru + Ru, Zr + Zr) show a consistent negative modulation with a significance of 1.9⁢𝜎, opposite in sign to that in 𝜌 0 → 𝜋 + ⁢𝜋 − photoproduction. This establishes for the first time that the interference sign is controlled by the spin structure of the final-state daughters, resolving the ambiguity present in the all-boson 𝜌 0 channel. The compact 𝐽/𝜓 probes gluon distributions at perturbative scales, resulting in a weaker modulation and providing stringent constraints on color glass condensate calculations. These findings demonstrate that spin-dependent interference in heavy vector mesons provides a new, experimentally accessible handle on gluon structure beyond traditional cross-section measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Mid-Range Science Objectives for the Event Horizon Telescope

The first images of the black holes in Sagittarius A* and M87* have created a wide range of new scientific opportunities in gravitational physics, compact objects, and relativistic astrophysics. We discuss here the scientific opportunities that arise from the rich data sets that have already been obtained and the new data sets that will be obtained, exploiting a wide range of technical advances, including observational agility, receiver upgrades, and the addition of new stations. This document provides a 5-year framework for Event Horizon Telescope (EHT) science structured around four fundamental questions that are used to prioritize the analysis of existing data, guide technical upgrades, and determine the optimal use of future observational opportunities with EHT, ALMA, and multi-wavelength facilities. Through enhancements over this period, the EHT will create the first movie of M87* connecting black hole and jet physics, provide detailed studies of the structure and dynamics of Sgr A*, characterize the magnetospheres of both systems through polarimetric imaging, and explore the spacetime properties of black holes with greater precision and range.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Mitigation of spatial nonstationarity with vision transformers

Spatial nonstationarity, the location variance of features’ statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in mineral deposits grades vary due to sedimentation and concentration processes, in hydrology rainfall varies due to the atmosphere and topography interactions, and in metallurgy crystalline structures vary due to differential cooling. Conventional geostatistical modeling workflows rely on the assumption of stationarity to be able to model spatial features for geostatistical inference. Nevertheless, this is often not a realistic assumption when dealing with nonstationary spatial data and this has motivated a variety of nonstationary spatial modeling workflows such as trend and residual decomposition, cosimulation with secondary features, and spatial segmentation and independent modeling over stationary subdomains. The advent of deep learning technologies has enabled new workflows for modeling spatial relationships. However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context. We demonstrate the impact of two common types of geostatistical spatial nonstationarity on deep learning model prediction performance and propose the mitigation of such impacts using self-attention (vision transformer) models. We demonstrate the utility of vision transformers for the mitigation of nonstationarity with relative errors as low as 10%, exceeding the performance of alternative deep learning methods such as convolutional neural networks. We establish best practice by demonstrating the ability of self-attention networks for modeling large-scale spatial relationships in the presence of commonly observed geospatial nonstationarity.

58 GEOSCIENCES↗

DOE OSTI2023

Cryo-EM structures reveal native GABA A receptor assemblies and pharmacology

Type A γ-aminobutyric acid receptors (GABA A Rs) are the principal inhibitory receptors in the brain and the target of a wide range of clinical agents, including anaesthetics, sedatives, hypnotics and antidepressants. However, our understanding of GABA A R pharmacology has been hindered by the vast number of pentameric assemblies that can be derived from 19 different subunits and the lack of structural knowledge of clinically relevant receptors. Here, we isolate native murine GABA A R assemblies containing the widely expressed α1 subunit and elucidate their structures in complex with drugs used to treat insomnia (zolpidem (ZOL) and flurazepam) and postpartum depression (the neurosteroid allopregnanolone (APG)). Using cryo-electron microscopy (cryo-EM) analysis and single-molecule photobleaching experiments, we uncover three major structural populations in the brain: the canonical α1β2γ2 receptor containing two α1 subunits, and two assemblies containing one α1 and either an α2 or α3 subunit, in which the single α1-containing receptors feature a more compact arrangement between the transmembrane and extracellular domains. Interestingly, APG is bound at the transmembrane α/β subunit interface, even when not added to the sample, revealing an important role for endogenous neurosteroids in modulating native GABA A Rs. Together with structurally engaged lipids, neurosteroids produce global conformational changes throughout the receptor that modify the ion channel pore and the binding sites for GABA and insomnia medications. Our data reveal the major α1-containing GABA A R assemblies, bound with endogenous neurosteroid, thus defining a structural landscape from which subtype-specific drugs can be developed.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Refinement for single-nanoparticle structure determination from low-quality single-shot coherent diffraction data

With the emergence of X-ray free-electron lasers, it is possible to investigate the structure of nanoscale samples by employing coherent diffractive imaging in the X-ray spectral regime. In this work, we developed a refinement method for structure reconstruction applicable to low-quality coherent diffraction data. The method is based on the gradient search method and considers the missing region of a diffraction pattern and the small number of detected photons. We introduced an initial estimate of the structure in the method to improve the convergence. The present method is applied to an experimental diffraction pattern of an Xe cluster obtained in an X-ray scattering experiment at the SPring-8 Angstrom Compact free-electron LAser (SACLA) facility. It is found that the electron density is successfully reconstructed from the diffraction pattern with a large missing region, with a good initial estimate of the structure. The diffraction pattern calculated from the reconstructed electron density reproduced the observed diffraction pattern well, including the characteristic intensity modulation in each ring. Our refinement method enables structure reconstruction from diffraction patterns under difficulties such as missing areas and low diffraction intensity, and it is potentially applicable to the structure determination of samples that have low scattering power.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Degradation of REBCO coated conductors due to a combination of epoxy impregnation, thermal cycles, and quench: Characteristics and a method of alleviation

REBa2Cu3Ox (REBCO) coated conductors are an enabling technology for >1 GHz nuclear magnetic resonance spectrometers that need >23.5 T solenoids, compact high-field fusion reactors beyond the International Thermonuclear Experimental Reactor, and 20 T class accelerator magnets. An in-depth understanding of stress and strain management is critically needed for these applications. Here, we report experimental data and an analysis of damages of REBCO-coated conductors in several scenarios relevant for their applications in high-field magnets and unique to their multi-layer thin film structure. The examined scenarios include failure induced by quench, failure induced by epoxy impregnation, and failure induced by a combination of epoxy impregnation and quench. The root cause is film blistering and peeling induced by thermally interfacial stresses characteristic of multilayered heteroepitaxial thin film structures on thick substrates utilized. The quantitative stress analysis is introduced using simple and easy to use formulas and clearly indicates how materials and structural characteristics affect the magnitude and the distribution of stresses and deflections. Finally, a simple method is introduced for removing degradation of REBCO coated conductors due to epoxy impregnation. Our data and analysis help our understanding of the practical limit of REBCO-coated conductors and can be utilized as a guidance for the physical design of REBCO coated conductors and high-current cables assembled from them and for minimizing damages.

Yin, Shijian (ORCID:0000000224312274)↗

DOE OSTI2024

Probing New Physics with Tau Leptons using the CMS Detector

The Standard Model (SM) of particle physics is a thoroughly well-tested theory of all electroweak scale phenomena to date—the most recent test being the discovery of the long-predicted Higgs boson. However, there is much evidence to suggest that it is incomplete. On the formal side, the mass of the Higgs particle itself is perplexingly small, while the largest theoretical energy scale of the SM is 17 orders of magnitude larger, a puzzle known as the hierarchy problem. On the experimental side, among other things, the SM lacks a candidate for dark matter, which is now known to play an indispensable role in astrophysical large scale structure formation. Many theories that address the fundamental puzzles of the SM predict the existence of multiple Higgs bosons, including a very light boson that couples preferentially to third-generation fermions. Observation of this new light boson, either directly produced or in the decay of the recently discovered SM Higgs, would provide an unmistakable sign of new dynamics in nature. The research supported by this award used data collected by the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) to explore boosted di-tau signatures of a new light boson as a tool in the quest to understand the shortcomings of the SM. With a proton-proton collision energy of 13 trillion electron-volts (TeV) and peak instantaneous proton luminosity of over 10^34 collisions per square centimeter per second (cm –2 s –1 ), the LHC is the only facility in the world that could enable this line of research. The research sought to address the hierarchy problem and other shortcomings of the SM through a mixture of LHC data analysis, maintenance of the CMS detector to ensure high quality data, and contributions to the Phase 2 upgrade detector for the upcoming High Luminosity phase of the LHC (HL-LHC).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Wireless Interrogation of High Temperature Surface Acoustic Wave Dynamic Strain Sensor

Dynamic strain sensing is necessary for high-temperature harsh-environment applications, including powerplants, oil wells, aerospace, and metal manufacturing. Monitoring dynamic strain is important for structural health monitoring and condition-based maintenance in order to guarantee safety, increase process efficiency, and reduce operation and maintenance costs. Sensing in high-temperature (HT), harsh-environments (HE) comes with challenges including mounting and packaging, sensor stability, and data acquisition and processing. Wireless sensor operation at HT is desirable because it reduces the complexity of the sensor connection, increases reliability, and reduces costs. Surface acoustic wave resonators (SAWRs) are compact, can operate wirelessly and battery-free, and have been shown to operate above 1000°C, making them a potential option for HT HE dynamic strain sensing. This paper presents wirelessly interrogated SAWR dynamic strain sensors operating around 288.8MHz at room temperature and tested up to 400°C. The SAWRs were calibrated with a high-temperature wired commercial strain gauge. The sensors were mounted onto a tapered-type Inconel constant stress beam and the assembly was tested inside a box furnace. The SAWR sensitivity to dynamic strain excitation at 25°C, 100°C, and 400°C was .439 μV/με, 0.363μV/με, and .136 μV/με, respectively. Furthermore, the experimental outcomes verified that inductive coupled wirelessly interrogated SAWRs can be successfully used for dynamic strain sensing up to 400°C.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Quantitative Microscopy Reveals Stepwise Alteration of Chromatin Structure during Herpesvirus Infection

During lytic herpes simplex virus 1 (HSV-1) infection, the expansion of the viral replication compartments leads to an enrichment of the host chromatin in the peripheral nucleoplasm. We have shown previously that HSV-1 infection induces the formation of channels through the compacted peripheral chromatin. Here, we used three-dimensional confocal and expansion microscopy, soft X-ray tomography, electron microscopy, and random walk simulations to analyze the kinetics of host chromatin redistribution and capsid localization relative to their egress site at the nuclear envelope. Our data demonstrated a gradual increase in chromatin marginalization, and the kinetics of chromatin smoothening around the viral replication compartments correlated with their expansion. We also observed a gradual transfer of capsids to the nuclear envelope. Later in the infection, random walk modeling indicated a gradually faster transport of capsids to the nuclear envelope that correlated with an increase in the interchromatin channels in the nuclear periphery. Our study reveals a stepwise and time-dependent mechanism of herpesvirus nuclear egress, in which progeny viral capsids approach the egress sites at the nuclear envelope via interchromatin spaces.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Separating the effects of nucleotide and EB binding on microtubule structure

Microtubules (MTs) are polymers assembled from αβ-tubulin heterodimers that display the hallmark behavior of dynamic instability. MT dynamics are driven by GTP hydrolysis within the MT lattice, and are highly regulated by a number of MT-associated proteins (MAPs). How MAPs affect MTs is still not fully understood, partly due to a lack of high-resolution structural data on undecorated MTs, which need to serve as a baseline for further comparisons. Here we report three structures of MTs in different nucleotide states (GMPCPP, GDP, and GTPγS) at near-atomic resolution and in the absence of any binding proteins. These structures allowed us to differentiate the effects of nucleotide state versus MAP binding on MT structure. Kinesin binding has a small effect on the extended, GMPCPP-bound lattice, but hardly affects the compacted GDP-MT lattice, while binding of end-binding (EB) proteins can induce lattice compaction (together with lattice twist) in MTs that were initially in an extended and more stable state. We propose a MT lattice-centric model in which the MT lattice serves as a platform that integrates internal tubulin signals, such as nucleotide state, with outside signals, such as binding of MAPs or mechanical forces, resulting in global lattice rearrangements that in turn affect the affinity of other MT partners and result in the exquisite regulation of MT dynamics.

Science & Technology - Other Topics↗

DOE OSTI2023

Design Criteria for High-Gradient Radio-Frequency Linacs

This article will review methods used at the SLAC National Accelerator Laboratory and other world accelerator laboratories to design high-gradient normal conducting accelerating structures. A quest for compact radio-frequency linacs fueled decades of studies toward a higher accelerating gradient. A major phenomena limiting the increase of the gradient is vacuum radio-frequency breakdown; therefore, this paper will address the breakdown physics and discuss approaches that reduce the breakdown probability. This discussion will cover both the electrical design and fabrication technology of the accelerating structures to achieve practical operating accelerating gradients in excess of 100 MV/m. Most of the data described here were obtained during the development of 11 GHz linacs for electron–positron linear colliders, so extrapolation of the results to other frequencies should be performed cautiously.

Dolgashev, Valery A. (ORCID:0000000166715402)↗

DOE OSTI2024

A JWST Survey of the Supernova Remnant Cassiopeia A

We present initial results from a James Webb Space Telescope (JWST) survey of the youngest Galactic core-collapse supernova remnant, Cassiopeia A (Cas A), made up of NIRCam and MIRI imaging mosaics that map emission from the main shell, interior, and surrounding circumstellar/interstellar material (CSM/ISM). We also present four exploratory positions of MIRI Medium Resolution Spectrograph integral field unit spectroscopy that sample ejecta, CSM, and associated dust from representative shocked and unshocked regions. Surprising discoveries include (1) a weblike network of unshocked ejecta filaments resolved to ~0.01 pc scales exhibiting an overall morphology consistent with turbulent mixing of cool, low-entropy matter from the progenitor's oxygen layer with hot, high-entropy matter heated by neutrino interactions and radioactivity; (2) a thick sheet of dust-dominated emission from shocked CSM seen in projection toward the remnant's interior pockmarked with small (~1'') round holes formed by ≲0$^"_•$1 knots of high-velocity ejecta that have pierced through the CSM and driven expanding tangential shocks; and (3) dozens of light echoes with angular sizes between ~0$^"_•$1 and 1' reflecting previously unseen fine-scale structure in the ISM. NIRCam observations place new upper limits on infrared emission (≲20 nJy at 3 μm) from the neutron star in Cas A's center and tightly constrain scenarios involving a possible fallback disk. These JWST survey data and initial findings help address unresolved questions about massive star explosions that have broad implications for the formation and evolution of stellar populations, the metal and dust enrichment of galaxies, and the origin of compact remnant objects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels

Abstract A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP’s analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of $$O(N^3)$$ O ( N 3 ) in computation and $$O(N^2)$$ O ( N 2 ) in storage. All existing methods addressing this issue utilize some form of approximation—usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user’s flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover—instead of induce—sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

GASP: Gradient-Aware Shortest Path Algorithm for Boundary-Confined 2-Manifold Reeb Graph Visualization

Reeb graphs are an important tool for abstracting and representing the topological structure of a function defined on a manifold. We have identified three properties for faithfully representing Reeb graphs in a visualization: they should be constrained to the boundary, compact, and aligned with the function gradient. Existing algorithms for drawing Reeb graphs are agnostic to or violate these properties. In this paper, we introduce an algorithm to generate Reeb graph visualizations, called GASP, that is cognizant of these properties, thereby producing visualizations that are more representative of the underlying data. To demonstrate the improvements, the resulting Reeb graphs are evaluated both qualitatively and quantitatively against the geometric barycenter algorithm, using its implementation available in the Topology ToolKit (TTK), a widely adopted tool for calculating and visualizing Reeb graphs.

Rahman, Sefat [University of Utah]↗