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DOE OSTI2025

Diel partitioning in microbial phosphorus acquisition in the Sargasso Sea

The daily cycle of photosynthetic primary production at the base of marine food webs is often limited by the availability of scarce nutrients. Microbial competition for these scarce resources can be alleviated insofar as the intensity of nutrient uptake and assimilation activities are distributed heterogeneously across organisms over periodic input cycles. Recent analysis of community transcriptional dynamics in the nitrogen-limited subtropical North Pacific gyre revealed evidence of temporal partitioning of nitrogen uptake and assimilation between eukaryotic phytoplankton, cyanobacteria, and heterotrophic bacteria over day-night cycles. Here, we present results from a Lagrangian metatranscriptomic time series survey in the Sargasso Sea and demonstrate temporally partitioned phosphorus uptake in this phosphorus-limited environment. In the Sargasso, heterotrophic bacteria, eukaryotic phytoplankton, and cyanobacteria express genes for phosphorus assimilation during the morning, day, and dusk, respectively. In conclusion, these results support the generality of temporal niche partitioning as an emergent mechanism that can structure uptake of limiting nutrients and facilitate coexistence of diverse microbes in open ocean ecosystems.

biological oceanography↗

DOE OSTI2017

Temporal Cyber Attack Detection.

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms require large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗

DOE OSTI2016

Modeling the Spatio-Temporal Variability in Subsurface Thermal Regimes Across a Low-Relief Polygonal Tundra Landscape: Modeling Archive

Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to atmosphere under warming climate. Ice--wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. The microtopography plays a critical role in regulating the fine scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behaviour under current as well as changing climate. We present here an end-to-end effort for high resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites at Barrow, Alaska spanning across low to transitional to high-centered polygon and representative of broad polygonal tundra landscape. A multi--phase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using high resolution LiDAR DEM, microtopographic features of the landscape were characterized and represented in the high resolution model mesh. Best available soil data from field observations and literature was utilized to represent the complex heterogeneous subsurface in the numerical model. This data collection provides the complete set of input files, forcing data sets and computational meshes for simulations using PFLOTRAN for four sites at Barrow Environmental Observatory. It also documents the complete computational workflow for this modeling study to allow verification, reproducibility and follow up studies. This dataset includes two zipped files and one .pdf file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

With recent technology advance in Internet of Things (IoT) that involves human, sensors, and mobile devices, networks are not only growing much larger but more complex and dynamic in nature. The spatiotemporal dynamics of networks are represented by both topological changes and temporal shifts of attribute information associated with network components. Understanding the pattern and trend of dynamic networks is increasingly important. While data mining approaches are generally useful in analyzing the statistical properties of networks, there has been recent trend to consider bringing human into the loop, and to examine how feedback from visualizations of large-scale dynamic networks can further improve data mining and machine learning. Traditional visualization methods based on animation and sequences of snapshot graphs are also limited by human cognitive capability. Here, we present a dynamic network analysis and visualization (DNAV) tool which explores the spatiotemporal dimensions of graph components. In particular, nodes and edges are augmented with spatiotemporal segmentation based on both topological and attribute dynamics (e.g., time and locations of connectivity). To further facilitate analysis of large dynamic networks, DNAV includes statistical dynamic overviews alongside graph views, as well as data filtering modules for scalable analysis. Using case studies on public datasets, we demonstrate the effectiveness of DNAV in understanding and analyzing anomalies in dynamic networks such as computer communication networks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Spectroscopic measurement of high-frequency electric fields in the interaction of explosive debris plasma with magnetized background plasma

The collision-less transfer of momentum and energy from explosive debris plasma to magnetized background plasma is a salient feature of various astrophysical and space environments. While much theoretical and computational work has investigated collision-less coupling mechanisms and relevant parameters, an experimental validation of the results demands the measurement of the complex, collective electric fields associated with debris-background plasma interaction. Emission spectroscopy offers a non-interfering diagnostic of electric fields via the Stark effect. A unique experiment at the University of California, Los Angeles, that combines the Large Plasma Device (LAPD) and the Phoenix laser facility has investigated the marginally super-Alfvénic, quasi-perpendicular expansion of a laser-produced carbon (C) debris plasma through a preformed, magnetized helium (He) background plasma via emission spectroscopy. Spectral profiles of the He II 468.6 nm line measured at the maximum extent of the diamagnetic cavity are observed to intensify, broaden, and develop equally spaced modulations in response to the explosive C debris, indicative of an energetic electron population and strong oscillatory electric fields. The profiles are analyzed via time-dependent Stark effect models corresponding to single-mode and multi-mode monochromatic (single frequency) electric fields, yielding temporally resolved magnitudes and frequencies. The proximity of the measured frequencies to the expected electron plasma frequency suggests the development of the electron beam-plasma instability, and a simple saturation model demonstrates that the measured magnitudes are feasible provided that a sufficiently fast electron population is generated during C debris–He background interaction. Potential sources of the fast electrons, which likely correspond to collision-less coupling mechanisms, are briefly considered.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

A fourth-order adaptive mesh refinement algorithm for the multicomponent, reacting compressible Navier–Stokes equations

In this study, we present a fourth-order in space and time block-structured adaptive mesh refinement algorithm for the compressible multicomponent reacting Navier–Stokes equations. The algorithm uses a finite-volume approach that incorporates a fourth-order discretisation of the convective terms. The time-stepping algorithm is based on a multi-level spectral deferred corrections method that enables explicit treatment of advection and diffusion coupled with an implicit treatment of reactions. The temporal scheme is embedded in a block-structured adaptive mesh refinement algorithm that includes subcycling in time with spectral deferred correction sweeps applied on levels. Here we present the details of the multi-level scheme paying particular attention to the treatment of coarse–fine boundaries required to maintain fourth-order accuracy in time. We then demonstrate the convergence properties of the algorithm on several test cases including both non-reacting and reacting flows. Finally we present simulations of a vitiated dimethyl ether jet in 2D and a turbulent hydrogen jet in 3D, both with detailed kinetics and transport.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

eCounter: Inline Per-IP Network Monitoring at Millisecond Resolution via eBPF

Scientific data acquisition (SciDAQ) systems are shifting from archive-based workflows to streaming paradigms, where real-time, fine-grained network monitoring becomes essential. While P4-enabled devices offer per-packet in-band observability, they require specialized switches and routers. Host-side tools like Prometheus exporters lack sufficient temporal granularity. To bridge this gap, we present eCounter, a lightweight, hardware-agnostic, inline telemetry agent built on extended Berkeley Packet Filter (eBPF). eCounter captures per-interface ingress and egress traffic, categorized by IP address and protocol, at millisecond to sub-millisecond resolution. In a 100 Gbps environment, it continuously exports up to 3,257 time-series bins per second with only 4% CPU utilization at a 35¿KiB/s data rate. We evaluate eCounter across diverse NIC MTU settings, hook types, CPU architectures and operating systems, and observed negligible impact on concurrent high-throughput streaming applications. Complexity analysis confirms that it can be readily scaled to distributed SciDAQ deployments.

Mei, Xinxin [Computational Sciences and Technology↗

DOE OSTI2019

The Zwicky Transient Facility: Surveys and Scheduler

Presented here is a novel algorithm for scheduling the observations of time-domain imaging surveys. Our integer linear programming approach optimizes an observing plan for an entire night by assigning targets to temporal blocks, enabling strict control of the number of exposures obtained per field and minimizing filter changes. A subsequent optimization step minimizes slew times between each observation. Our optimization metric self-consistently weights contributions from time-varying airmass, seeing, and sky brightness to maximize the transient discovery rate. We describe the implementation of this algorithm on the surveys of the Zwicky Transient Facility and present its on-sky performance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

SU-G-JeP2-10: On the Need for a Dynamic Model for Patient-Specific Distortion Corrections for MR-Only Pelvis Treatment Planning

Purpose: Patient-specific distortions, particularly near tissue/air interfaces, require assessment and possible corrections for MRI-only radiation treatment planning (RTP). However, patients are dynamic due to changes in physiological status and motion during imaging sessions. This work investigated the need for dynamic patient-specific distortion corrections to support pelvis MR-only RTP. Methods: The pelvises of healthy volunteers were imaged at 1.0T, 1.5T, and 3.0T. Patient-specific distortion field maps were generated using a dual-echo gradient-recalled echo (GRE) sequence with B0 field maps obtained from the phase difference between the two echoes acquired at two timepoints: empty and full bladders. To quantify changes arising from respiratory state, end-inhalation and end-expiration data were acquired. Distortion map differences were computed between the empty/full bladder and inhalation/expiration to characterize local changes. The normalized frequency distortion distributions in T2-weighted TSE images were characterized, particularly for simulated prostate planning target volumes (PTVs). Results: Changes in rectal and bowel air location were observed, likely due to changes in bladder filling. Within the PTVs, displacement differences (mean ± stdev, range) were −0.02 ± 0.02 mm (−0.13 to 0.07 mm) for 1.0T, −0.1 ± 0.2 mm (−0.92 to 0.74 mm) for 1.5T, and −0.20 ± 0.03 mm (−0.61 to 0.38 mm) for 3.0T. Local changes of ∼1 mm at the prostate-rectal interface were observed for an extreme case at 1.5T. For end-inhale and end-exhale scans at 3.0T, 99% of the voxels had Δx differences within ±0.25mm, thus the displacement differences due to respiratory state appear negligible in the pelvis. Conclusion: Our work suggests that transient bowel/rectal gas due to bladder filling may yield non-negligible patient-specific distortion differences near the prostate/rectal interface, whereas respiration had minimal effect. A temporal patient model for patient-specific distortion corrections may be advantageous for MR-only RTP, although further investigations in larger cohorts are needed to fully characterize distortion magnitude. The submitting institution has research agreements with Philips Healthcare. Research sponsored by a Henry Ford Health System Internal Mentored Grant.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Identifying emerging phenomenon in long temporal phenotyping experiments

Abstract Motivation The rapid improvement of phenotyping capability, accuracy and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. Results We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust and biologically significant. Availability and implementation The source code, manual and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗

DOE OSTI2015

Discovering multi-scale co-occurrence patterns of asthma and influenza with the Oak Ridge bio-surveillance toolkit

Here, we describe a data-driven unsupervised machine learning approach to extract geo-temporal co-occurrence patterns of asthma and the flu from large-scale electronic healthcare reimbursement claims (eHRC) datasets. Specifically, we examine the eHRC data from 2009 to 2010 pandemic H1N1 influenza season and analyze whether different geographic regions within the United States (US) showed an increase in co-occurrence patterns of the flu and asthma. Our analyses reveal that the temporal patterns extracted from the eHRC data show a distinct lag time between the peak incidence of the asthma and the flu. While the increased occurrence of asthma contributed to increased flu incidence during the pandemic, this co-occurrence is predominant for female patients. The geo-temporal patterns reveal that the co-occurrence of the flu and asthma are typically concentrated within the south-east US. Further, in agreement with previous studies, large urban areas (such as New York, Miami, and Los Angeles) exhibit co-occurrence patterns that suggest a peak incidence of asthma and flu significantly early in the spring and winter seasons. Together, our data-analytic approach, integrated within the Oak Ridge Bio-surveillance Toolkit platform, demonstrates how eHRC data can provide novel insights into co-occurring disease patterns.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Sub-nanosecond time-resolved ambient-pressure X-ray photoelectron spectroscopy setup for pulsed and constant wave X-ray light sources

An apparatus for sub-nanosecond time-resolved ambient-pressure X-ray photoelectron spectroscopy studies with pulsed and constant wave X-ray light sources is presented. A differentially pumped hemispherical electron analyzer is equipped with a delay-line detector that simultaneously records the position and arrival time of every single electron at the exit aperture of the hemisphere with ∼0.1 mm spatial resolution and ∼150 ps temporal accuracy. The kinetic energies of the photoelectrons are encoded in the hit positions along the dispersive axis of the two-dimensional detector. Pump-probe time-delays are provided by the electron arrival times relative to the pump pulse timing. An average time-resolution of (780 ± 20) ps (FWHM) is demonstrated for a hemisphere pass energy E{sub p} = 150 eV and an electron kinetic energy range KE = 503–508 eV. The time-resolution of the setup is limited by the electron time-of-flight (TOF) spread related to the electron trajectory distribution within the analyzer hemisphere and within the electrostatic lens system that images the interaction volume onto the hemisphere entrance slit. The TOF spread for electrons with KE = 430 eV varies between ∼9 ns at a pass energy of 50 eV and ∼1 ns at pass energies between 200 eV and 400 eV. The correlation between the retarding ratio and the TOF spread is evaluated by means of both analytical descriptions of the electron trajectories within the analyzer hemisphere and computer simulations of the entire trajectories including the electrostatic lens system. In agreement with previous studies, we find that the by far dominant contribution to the TOF spread is acquired within the hemisphere. However, both experiment and computer simulations show that the lens system indirectly affects the time resolution of the setup to a significant extent by inducing a strong dependence of the angular spread of electron trajectories entering the hemisphere on the retarding ratio. The scaling of the angular spread with the retarding ratio can be well approximated by applying Liouville's theorem of constant emittance to the electron trajectories inside the lens system. The performance of the setup is demonstrated by characterizing the laser fluence-dependent transient surface photovoltage response of a laser-excited Si(100) sample.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. Here, a comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Direct Numerical Simulation of Particle Behaviour in a Gas-Solid Three Dimensional Plane Jet

In this paper, direct numerical simulations (DNS) of a three-dimensional (3D), non-reacting, temporally evolving planar jet laden with mono-dispersed solid particles in the two-way coupling (TWC) regime are performed. Three different particles Stokes numbers (St = 0.1, 1, 10) have been considered. This has been achieved by varying the particle diameter while keeping the particle mass loading (fm = 1) and the jet Reynolds number (Rejet = 2000) unchanged. The objective is to study the effect of the particle Stokes number TWC regime on the temporal development of the planar jet. Two-way coupled momentum and heat transfer has been studied by investigating mean relative velocity and temperature. Results indicate that the relative parameters are more pronounced on the edges of the jet and decrease in time in general. At the center of the jet however, the mean value first increases and then decreases again. Additionally, lighter particles spread farther than heavier particles from the center of the jet. Furthermore, the heavier particles delay the development of the jet due to TWC effects.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Characterization of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy system

Purpose: To characterize the performance of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy (MR-IGRT) system. Methods: The imaging performance characterization included four components: ACR (the American College of Radiology) phantom test, spatial integrity, coil signal to noise ratio (SNR) and uniformity, and magnetic field homogeneity. The ACR phantom test was performed in accordance with the ACR phantom test guidance. The spatial integrity test was evaluated using a 40.8 × 40.8 × 40.8 cm{sup 3} spatial integrity phantom. MR and computed tomography (CT) images of the phantom were acquired and coregistered. Objects were identified around the surfaces of 20 and 35 cm diameters of spherical volume (DSVs) on both the MR and CT images. Geometric distortion was quantified using deviation in object location between the MR and CT images. The coil SNR test was performed according to the national electrical manufacturers association (NEMA) standards MS-1 and MS-9. The magnetic field homogeneity test was measured using field camera and spectral peak methods. Results: For the ACR tests, the slice position error was less than 0.10 cm, the slice thickness error was less than 0.05 cm, the resolved high-contrast spatial resolution was 0.09 cm, the resolved low-contrast spokes were more than 25, the image intensity uniformity was above 93%, and the percentage ghosting was less than 0.22%. All were within the ACR recommended specifications. The maximum geometric distortions within the 20 and 35 cm DSVs were 0.10 and 0.18 cm for high spatial resolution three-dimensional images and 0.08 and 0.20 cm for high temporal resolution two dimensional cine images based on the distance-to-phantom-center method. The average SNR was 12.0 for the body coil, 42.9 for the combined torso coil, and 44.0 for the combined head and neck coil. Magnetic field homogeneities at gantry angles of 0°, 30°, 60°, 90°, and 120° were 23.55, 20.43, 18.76, 19.11, and 22.22 ppm, respectively, using the field camera method over the 45 cm DSV. Conclusions: The onboard imaging unit of the first commercial MR-IGRT system meets ACR, NEMA, and vendor specifications.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-D-12A-05: Iterative Reconstruction Techniques to Enable Intrinsic Respiratory Gated CT in Mice

Purpose: Longitudinal studies of lung function in mice need the ability to image different phases of ventilation in free-breathing mice using retrospective gating. However, retrospective gating often produces under-sampled and uneven angular samples, resulting in severe reconstruction artifacts when using traditional FDK based reconstruction algorithms. We wanted to demonstrate the utility of iterative reconstruction method to enable intrinsic respiratory gating in small-animal CT. Methods: Free-breathing mice were imaged using a Siemens Inveon PET/micro-CT system. Evenly distributed projection images were acquired at 360 angles. Retrospective respiratory gating was performed using an intrinsic marker based on the average intensity in a region covering the diaphragm. Projections were classified into 4 and 6 phases (finer temporal resolution) resulting in 138 and 67 projections respectively. Reconstruction was carried out using 3 Methods: conventional FDK, iterative penalized least-square (PWLS) with total variation (TV), and PWLS with edge-preserving penalty. The performance of the methods was compared using contrast-to-noise (CNR) in a region of interest (ROI). Line profile through a specific region was plotted to evaluate the preserving of edges. Results: In both the cases with 4 and 6 phases, inadequate and non-uniform angular sampling results in artifacts using conventional FDK. However, such artifacts are minimized using both the iterative methods. Using both 4 and 6 phases, the iterative techniques outperformed FDK in terms of CNR and maintaining sharp edges. This is further evidenced especially with increased artifacts using FDK for 6 phases. Conclusion: This work indicates fewer artifacts and better image details can be achieved with iterative reconstruction methods in non-uniform under-sampled reconstruction. Using iterative methods can enable free-breathing intrinsic respiratory gating in small-animal CT. Further studies are needed to compare the computational complexity for large datasets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Nonoverlapping Localized Exponential Time Differencing Methods for Diffusion Problems

In this paper, we propose nonoverlapping localized exponential time differencing (ETD) methods for diffusion problems. The model time-dependent diffusion equation is first reformulated on subdomains based on the nonoverlapping domain decomposition, in which Neumann boundary conditions are imposed on the interfaces for the subdomain problems and Dirichlet type conditions are enforced to form a space-time interface problem. After spatial discretization by standard central finite differences and temporal integration with the first or second order ETD methods, the fully discrete interface problem is obtained. Such an interface problem is then solved iteratively either at each time step or over the whole time interval: the former involves the solution of stationary problems in each subdomain at each iteration while the latter involves the solution of time-dependent subdomain problems at each iteration. For both approaches, we prove that localized ETD solutions conserve mass exactly and converge in time to the exact space semidiscrete solution. Numerical experiments in two dimensions are also presented to illustrate the performance of the proposed methods.

Mass conservation↗