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Results for “spatial-temporal modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCC FLUS and LULCC Demeter , respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCC FLUS and LULCC Demeter , and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Physics-Informed Graph Neural Networks for Collaborative Dynamic Reconfiguration and Voltage Regulation in Unbalanced Distribution Systems

Network reconfiguration has long been employed as a strategic approach to minimize power distribution system losses and effectively regulate voltage levels. Tap-changing voltage regulators are also critical for controlling bus voltages, especially in accommodating the increasing integration of distributed energy resources (DERs) with intermittent outputs. This paper introduces novel methodologies to address the challenges of dynamic reconfiguration and optimal tap setting in unbalanced three-phase distribution systems. We propose an approximated mixed-integer quadratically constrained program (MIQCP) to model dynamic reconfiguration, along with a pioneering formulation for voltage regulator (VR) tap-setting based on Special Ordered Set type 1 (SOS1). To mitigate computational complexity, we propose a physics-informed spatial-temporal graph convolutional network (STGCN) with an integrated link classifier. The proposed approach enables efficient solution generation by fixing specific variables in the MIQCP instance and solving the simplified sub-MIP using an MIP solver. Numerical studies demonstrate the superior prediction accuracy of our STGCN model compared to baseline neural network models, resulting in reduced DER curtailment and voltage deviation with shorter computation time.

dynamic reconfiguration↗
DOE OSTI2024

Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation

Here, we present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quasinormal Coupled-Mode Analysis of Dynamic Gain in Exceptional-Point Lasers

One of the key features of lasers operating near exceptional points (EPs) is that the gain medium can support an oscillating population inversion above a pump threshold, leading to self-modulated laser dynamics. This unusual behavior opens up new possibilities for frequency comb generation and temporal modulation. However, the dynamic population inversion couples signals with different frequencies and is difficult to capture using conventional temporal coupled-mode theory (TCMT) based on stationary saturable gain. In this paper, we develop a perturbative coupled-mode analysis framework to capture the spatial-temporal dynamics of near-EP lasers. By decomposing discrete frequency generation into multiple excitations of resonant modes, our analysis establishes a minimal physical model that translates the local distribution of dynamic population-inversion into a resonant modal interpretation of laser gain. Furthermore, this work enables the exploration of unique properties in this self-time-modulated systems, such as time-varying scattering and nonreciprocal transmission.

Absorption↗
DOE OSTI2025

Detecting outbreaks using a spatial latent field

In this paper, we present a method for estimating the infection-rate of a disease as a spatial-temporal field. Our data comprises time-series case-counts of symptomatic patients in various areal units of a region. We extend an epidemiological model, originally designed for a single areal unit, to accommodate multiple units. The field estimation is framed within a Bayesian context, utilizing a parameterized Gaussian random field as a spatial prior. We apply an adaptive Markov chain Monte Carlo method to sample the posterior distribution of the model parameters condition on COVID-19 case-count data from three adjacent counties in New Mexico, USA. Our results suggest that the correlation between epidemiological dynamics in neighboring regions helps regularize estimations in areas with high variance (i.e., poor quality) data. Using the calibrated epidemic model, we forecast the infection-rate over each areal unit and develop a simple anomaly detector to signal new epidemic waves. Our findings show that anomaly detector based on estimated infection-rates outperforms a conventional algorithm that relies solely on case-counts.

Safta, Cosmin [Sandia National Laboratories (SNL-C↗
DOE OSTI2023

A Continental-Scale Estimate of Soil Organic Carbon Change at NEON Sites and Their Environmental and Edaphic Controls

Current carbon cycle models focus on the effects of climate and land-use change on primary productivity and microbial-mineral dependent carbon turnover in the topsoil, while less attention has been paid to vertical soil processes and soil-dependent response to land-use change along the profile. In this study, a spatial-temporal analysis was used to estimate soil organic carbon (SOC) change in topsoil/A horizon and subsoil/B horizon at National Ecological Observatory Network (NEON) sites, USA over 30 years. To separate the effects of land-use, environmental, and edaphic factors on SOC change, space-for-time substitution was used in combination with the Continuous Change Detection and Classification algorithm and Structural Equation Modeling. Results showed that (a) under natural vegetation, Spodosols and Inceptisols found in the eastern NEON sites had substantial topsoil SOC accumulation (+0.4 to +1.2 Mg C ha –1 year –1 ), while Inceptisols and Andisols in the west had a comparable magnitude of topsoil SOC loss (–0.5 to –1.8 Mg C ha –1 year –1 ); (b) Mollisols and Alfisols in the Central Plains sites were susceptible to significant SOC loss under farming and grazing; (c) Runoff/erosion and leaching potential, vertical translocation, and mineral sorption were the most important factors controlling SOC variation across the NEON sites. Our work could be used to parameterize ecosystem models simulating SOC change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Towards Diverse and Representative Global Pretraining Datasets for Remote Sensing Foundation Models

The design of a pretraining dataset is emerging as a critical component for the generality of foundation models. In the remote sensing realm, large volumes of imagery and benchmark datasets exist that can be leveraged to pretrain foundation models, however using this imagery in absence of a well-crafted sampling strategy is inefficient and has the potential to create biased and less generalizable models. Here, we provide a discussion and vision for the curation and assessment of pretraining datasets for remote sensing geospatial foundation models. We highlight the importance of geographic, temporal, and image acquisition diversity and review possible strategies to enable such diversity at global scale. In addition to these characteristics, support for various spatial-temporal pretext tasks within the dataset is also critical. Ultimately, our primary objective is to place emphasis on and draw attention to the data curation stage of the foundation model development pipeline. By doing so, we think it is possible to reduce biases of geospatial foundation models, as well as enable broader generalization to downstream remote sensing tasks and applications.

Arndt, Jacob↗
DOE OSTI2023

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.

deep neural network↗
DOE OSTI2025

Thermal Characterization and Exhumation of Northwest San Juan Basin Area, NM

The San Juan Basin (SJB), located in southwestern Colorado northwestern New Mexico, containing Paleozoic through middle Cenozoic strata, formed as a partitioned basin approximately 80 Ma in response to the Laramide orogeny. The SJB is a commercially mature, petroleum-producing basin, and currently being explored for CCUS and geothermal resources. Modern heat flow within the SJB is spatially variable with higher geothermal gradients in its northern and eastern portions. The temporal history of the basin thermal history is essential for understanding capacity for carbon storage and geothermal exploration. We present thermochronometric analyses of two surface and two subsurface samples to constrain spatial-temporal thermal evolution of the northwestern San Juan Basin. Apatite (U-Th)/He thermochronometric analyses were conducted on Cliff House and Kirtland Formation outcrops and subsurface samples of the Ojo Alamo and Pictured Cliffs Formations from a DOE funded, CCUS project pilot-well. In addition, vitrinite reflectance data and 1D basin modelling constrain possible time-temperature pathways of the samples in an area where no previous studies have constrained the uplift/exhumation of the Hogback Monocline using thermochronometry. Modeling suggests that elevated subsurface temperatures developed simultaneously with regional late Oligiocene volcanism in the San Juan Volcanic field and timing of maximum burial in the SJB. Late Miocene to Pliocene cooling/exhumation through the apatite (U-Th)/He partial retention zone support stratigraphic evidence of the Colorado Plateau uplift, possibly regional epeirogenic uplift driven by mantle processes.

02 PETROLEUM↗
DOE OSTI2023

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Nature-GL: A Revolutionary Learning Paradigm Unleashing Nature’s Power in Real-World Spatial-Temporal Graph Learning

Spatial-Temporal Graph Learning (ST-GL) is a prominent research area due to its unique capability to effectively learn real-world graphs. Applications of ST-GL pose stringent and various demands on not only real-time inference with low energy cost and high ac- curacy but also fast training. Unfortunately, as Moore’s Law approaches its limits and ST-GL model complexity drastically grows, the gap between digital hardware’s computational power and ST- GL application demands is widening. In response, this paper introduces Nature-GL, a nature-powered graph learning paradigm that exploits the principle of entropy increase to advance graph learning. In particular, Nature-GL transforms both the training and inference of real-valued ST-GL into electron-speed natural anneal- ing processes of a parameterized dynamical system that represents the target graphs. Experimental results across four real-world ap- plications with six datasets demonstrate that Nature-GL achieves orders-of-magnitude speedups in both training and inference, delivering higher accuracy compared to Graph Neural Networks.

Liu, Chuan [University of Rochester]↗
DOE OSTI2025

Non-propagating structures and propagating waves in solar wind turbulence revealed by simulations and observations

Structures and waves are common features of solar wind turbulence at various scales. The interplay between structures and waves is important for processes such as the turbulent energy cascade, plasma heating, and particle scattering. Our understanding of turbulence has been advanced by not only new space missions and numerical simulations, but also techniques that have been developed to interpret the rapidly growing turbulence data. We review basic models of turbulence with a specific focus on the analysis methods for understanding magnetic structures and waves. MHD and kinetic waves in single-spacecraft time series measurements can be identified through mode decomposition or their characteristic polarization signatures. The structures in this paper are considered as zero-frequency, non-propagating or convected modes embedded in the solar wind. The synergy between observations and simulations is most evident in the application of spatial-temporal analysis to multi-spacecraft observation and turbulence simulations. The spatial-temporal analysis has greatly improved our understanding of structures and waves in turbulence. We conclude by discussing prospects for future research.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Interpretable Models for Workflow Differentiation in High-Performance Scientific Networks

Scientific workflows in high-performance networks spawn hundreds of interdependent flows that must be managed collectively—yet existing network classifiers treat each flow in isolation, leading to fragmented QoS decisions and missed interflow patterns. We present a novel traffic classification solution that operates at the workflow level, distinguishing entire filetransfer operations from streaming analytics by capturing how concurrent flows interact and burst together. We introduce a workflow identification window (WIW) that ingests raw packet headers from parallel flows into unified tensors, preserving the spatial-temporal patterns that differentiate scientific workflows. This approach achieves 98.7% accuracy using CNN, LSTM, and hybrid architectures, while maintaining 84% accuracy on production traffic collected a week later—demonstrating robustness to temporal drift. By integrating SHAP and GradCAM explainability, we reveal that early-packet timing patterns and cross-flow correlations drive classification decisions, providing operators with interpretable insights. Our system enables coherent workflow-level QoS enforcement and dynamic bandwidth allocation in scientific networks, eliminating manual per-flow configuration while maintaining classification latency at millisecond level.

Giannakou, Anna [LBL, Berkeley]↗
DOE OSTI2023

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. Here, this paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Utah FORGE 3-2535: Joint EM-Seismic-InSAR Imaging of Fracture Properties - Workshop Presentation

This is a presentation on the Joint Electromagnetic/Seismic/InSAR Imaging of Spatial-Temporal Fracture Growth and Estimation of Physical Fracture Properties During EGS Resource Development project by Lawrence Berkeley National Laboratory, presented by Dr. David Alumbaugh, Staff Scientist. The project's objective was to develop a set of technologies and workflow to image induced fracture generation and growth for an Enhanced Geothermal System (EGS). The project anticipates imaging of the fracture generation and growth at FORGE using a combination of passive seismic, active source borehole EM, and INSAR technology. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗
DOE OSTI2015

On the thermodynamics and other constitutive properties of a class of strongly magnetized matter observed in astrophysics

It is shown that the occurrence of magnetization work is a consistent thermodynamic explanation of the property of anti-correlation between temperature and density of the electrons gas in a class of magnetic-field-dominated structures observed in the interplanetary medium. In this model, a 7/4 scaling ratio for magnetization work to electron-gas work explains the often observed anomalous adiabatic polytropic exponent γ{sub a}=1/2. This interpretation is built on the theoretical conjecture of a matter state having spatial confinement of most hadronic elements of matter, i.e., matter held in place by the action of what is here denominated as a “super-strong” magnetic field, which together with the plasma it contains satisfies—on medium to large spatial-temporal scales—ideal magnetohydrodynamics. Several elements of the interpretation are tested for a case study, the flux-rope (FR) structure passing Wind SC on 1998 June 2. This allows us to extract, for a 185 s sample interval inside the FR, the following constitutive properties of this diamagnetic state of matter: (i) sound speed, (ii) thermal temperature, (iii) magnetic permeability, and (iv) a low limit to its dielectric permittivity. The intervals of coherence, i.e., thermodynamic homogeneity, extend from a few to many 10{sup 4} km for plasma and magnetic field average with a sampling rate of 3s per value. We point out that this state of matter, which we identify to be an amorphous three-dimensional Langmuir lattice, differs from other materials studied in the laboratory at extreme low temperatures and is well described as BCS-superconductors because in our case we understand that (a) the magnetic permeability is non-zero, and (b) substantial field-aligned, convected-current density exists.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Spatial-Temporal PV Hosting Capacity Estimation and Evaluation

Evaluating Photovoltaic Hosting Capacity (PVHC) is an essential step in the process of integrating solar energy into power grids, particularly when focusing on the distribution network (DN) as the primary integration target. PVHC needs to be investigated, especially in cases where the grids are unbalanced, and their operational conditions vary spatially and temporally. This motivation prompted us to propose a scalable model tailored to this application. In this paper, we applied linearization to the alternating current optimal power flow (AC-OPF) and solar inverters, transforming the original problem into a mixed-integer linear programming (MILP) problem. Additionally, we accounted for the battery energy storage system (BESS) as a time-coupling factor for calculating PVHC. We then compared the PVHC results between the IEEE-13 bus and SMART-DS San Francisco (SFO) cases and discussed the extent to which BESS can enhance the PVHC of a DN. Furthermore, we designed a web-based graphical visualization for the SFO case, enabling user interaction with raw data and simulation results on a map through a graphical user interface (GUI). In summary, our results and findings provide valuable insights for future three-phase unbalanced AC-OPF PVHC practices and their visualization.

AC-optimal power flow↗