SEARCH · Engineering Papers

Results for “temporal”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗

DOE OSTI2026

Assessing Cloud and Precipitation Properties on Temporal and Spatial Scales Using LASSO Simulations over ENA

Low clouds and precipitation representation remain a major source of uncertainty in Earth System Models (ESMs), particularly due to challenges in representing their sub-grid variability and scale-dependent sampling. This study evaluates the performance of preliminary simulations from the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project over the Eastern North Atlantic (ENA), with a focus on liquid water path (LWP), ice water path (IWP), cloud fraction (CF), and surface precipitation simulated across closed-cell, open-cell, and transitional cloud regimes. Using LES (100 m horizontal grid spacing) driven by ERA5 and MERRA-2 reanalyses, we assess the representativeness of ground-based point observations by analyzing their correspondence to model-resolved spatial and temporal means. Results suggest that observational sampling of at least 6 hours is required to achieve consistency with domain-scale averages, in particular for observations that exhibit pronounced sub-grid heterogeneity, such as precipitation. ERA5-forced simulations exhibit improved spatial coherence and agreement with domain-averaged quantities when compared to MERRA-2 runs, with performance discrepancies largest for convective cloud conditions due to differences in forcing fidelity and temporal resolution. These findings highlight the importance of regime-aware model evaluation strategies and potentially demonstrate how LES can inform observation-model comparison practices and the development of cloud and precipitation parameterizations in ESMs.

Liang, Jiakun [University of Hawai'i at Manoa] (OR↗

NASA NTRS1986

Temporal sensitivity

Human visual temporal sensitivity is examined. The stimuli used to measure temporal sensitivity are described and the linear systems theory is reviewed in terms of temporal sensitivity. A working model which represents temporal sensitivity is proposed. The visibility of a number of temporal wave forms, sinusoids, rectangular pulses, and pulse pairs, is analyzed. The relation between spatial and temporal effects is studied. Temporal variations induced by image motion and the effects of light adaptation on temporal sensitivity are considered.

Watson, Andrew B.↗

DOE OSTI2025

Spatial and Temporal Variability of Vertical Velocity under Shallow Cumulus

Vertical velocity distribution below cloud is one of the key determinants of cloud life cycle, but observations of this variable are extremely sparse in space. Doppler lidar retrievals and large-eddy simulations at the U.S. Department of Energy’s Atmospheric Radiation Measurement User Facility Southern Great Plains site are used to determine whether vertical velocity statistics from temporally dense profiles at a single location can be substituted for spatial vertical velocity statistics. We show that even a small number (five) of widely distributed [ O (1°) latitude/longitude spacing] lidars is sufficient sampling to reconstruct domainwide spatial vertical velocity variance, but not higher moments of the vertical velocity distribution. Spatial and temporal vertical velocity variances in the Doppler lidar observations are nearly interchangeable as long as the spatial variance is temporally averaged and the temporal variance is averaged across lidars. This is true even though the dominant spatial scales of vertical velocity variability are ≲ 3 km, more than an order of magnitude smaller than the spacing between the lidars. Further, in the limit where the temporal variance does not vary across a spatial domain (e.g., if the meteorological and surface forcing of the atmospheric turbulence is homogeneous across the domain) and the domain-mean vertical velocity is zero, the commonly available retrieval of temporal vertical velocity variance at one site is equivalent to the spatial variance over the domain. We use an updraft parcel model to show that substituting temporal for spatial vertical velocity statistics will have a relatively minor effect on cloud droplet number concentrations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Properties of the Residual Stress of the Temporally Filtered Navier-Stokes Equations

The development of a unifying framework among direct numerical simulations, large-eddy simulations, and statistically averaged formulations of the Navier-Stokes equations, is of current interest. Toward that goal, the properties of the residual (subgrid-scale) stress of the temporally filtered Navier-Stokes equations are carefully examined. Causal time-domain filters, parameterized by a temporal filter width 0 less than Delta less than infinity, are considered. For several reasons, the differential forms of such filters are preferred to their corresponding integral forms; among these, storage requirements for differential forms are typically much less than for integral forms and, for some filters, are independent of Delta. The behavior of the residual stress in the limits of both vanishing and in infinite filter widths is examined. It is shown analytically that, in the limit Delta to 0, the residual stress vanishes, in which case the Navier-Stokes equations are recovered from the temporally filtered equations. Alternately, in the limit Delta to infinity, the residual stress is equivalent to the long-time averaged stress, and the Reynolds-averaged Navier-Stokes equations are recovered from the temporally filtered equations. The predicted behavior at the asymptotic limits of filter width is further validated by numerical simulations of the temporally filtered forced, viscous Burger's equation. Finally, finite filter widths are also considered, and a priori analyses of temporal similarity and temporal approximate deconvolution models of the residual stress are conducted.

Pruett, C. D.↗

NASA NTRS2018

Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation

Recently, the makespan-minimization problem of compiling a general class of quantum algorithms into near-term quantum processors has been introduced to the AI community. The research demonstrated that temporal planning is a strong solution approach for the studied class of quantum circuit compilation (QCC) problems. In this paper, we explore the use of methods from operations research, specifically constraint programming (CP), as an alternative and complementary approach to temporal planning. We also extend previous work by introducing two new problem variations that incorporate important characteristics identified by the quantum computing community. We apply temporal planning and CP to the baseline and extended QCC problems as both stand-alone and hybrid approaches. The hybrid method uses solutions found by temporal planning to warm-start CP, leveraging the ability of temporal planning to find satisficing solutions to problems with a high degree of task optionality, an area that CP typically struggles with. These solutions are then used to seed the CP formulation which significantly benefits from inferred bounds on planning horizon and task counts provided by the warm-start. Our extensive empirical evaluation indicates that while stand-alone CP is not competitive with temporal planning, except for the smallest problems, CP in a hybrid setting is beneficial for all temporal planners in all problem classes.

quantum circuit compilation↗

DOE OSTI2024

MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior involves optimizing data layout and/or restructuring code, and requires studying spatial-temporal data locality. Existing data locality analyses focus on single-location metrics and are restricted to evaluating temporal locality. We introduce spatial-temporal affinity metrics that quantify temporal access proximity, forward access correlation, and nearby access correlation between pairs of memory locations. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity at multiple resolutions (3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and that be used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for signature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algorithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance when contrasted with reuse distance analysis.

Suriyakumar, Yasodhadevi↗

NASA NTRS1983

The use of LANDSAT-4 MSS digital data in temporal data sets and the evaluation of scene-to-scene registration accuracy

The MSS sensor on LANDSAT 4 is, in certain performance aspects, different from those on LANDSATS 1 through 3. These differences created some concern in the NASA research community as to whether individual data sets can be registered accurately enough to produce acceptable data sets for multitemporal data analysis. The use of LANDSAT 4 MSS digital data in temporal data sets is examined and a method is presented for estimating temporal registration accuracy based on the use of an X-Y digitizer and grey tone electrostatic plots. Results indicate that the RMS temporal registration errors are not significantly different from the temporal data sets generated using LANDSAT 4 and LANDSAT 2 data (33.35 meters) and the temporal data set constructed from two LANDSAT 2 data sets (33.61 meters). A derivation of the model used to evaluate the temporal registration is included.

Anderson, J. E.↗

NASA NTRS1985

The use of Landsat-4 MSS digital data in temporal data sets and the evaluation of scene-to-scene registration accuracy

The MSS sensor on Landsat 4 is, in certain performance aspects, diferent from those of Landsats 1 through 3. These differences created some concern in the NASA research community as to whether individual data sets can be registered accurately enough to produce acceptable data sets for multitemporal data analysis. The use of Landsat 4 MSS digital data in temporal data sets is examined and a method is presented for estimating temporal registration accuracy based on the use of an X-Y digitizer and grey tone electrostatic plots. Results indicate that the RMS temporal registration errors are not significantly different from the temporal data sets generated using Landsat 4 and Landsat 2 data (33.35 meters) and the temporal data set constructed from two Landsat 2 data sets (33.61 meters). A derivation of the model used to evaluate the temporal registration is included.

Anderson, J. E.↗

NASA NTRS1992

Incorporation of varying types of temporal data in a neural network

Most neural network models do not specifically deal with temporal data. Handling of these variables is complicated by the different uses to which temporal data are put, depending on the application. Even within the same application, temporal variables are often used in a number of different ways. In this paper, types of temporal data are discussed, along with their implications for approximate reasoning. Methods for integrating approximate temporal reasoning into existing neural network structures are presented. These methods are illustrated in a medical application for diagnosis of graft-versus-host disease which requires the use of several types of temporal data.

Cohen, M. E.↗

NASA NTRS1995

A simulation to study the feasibility of improving the temporal resolution of LAGEOS geodynamic solutions by using a sequential process noise filter

A key drawback to estimating geodetic and geodynamic parameters over time based on satellite laser ranging (SLR) observations is the inability to accurately model all the forces acting on the satellite. Errors associated with the observations and the measurement model can detract from the estimates as well. These 'model errors' corrupt the solutions obtained from the satellite orbit determination process. Dynamical models for satellite motion utilize known geophysical parameters to mathematically detail the forces acting on the satellite. However, these parameters, while estimated as constants, vary over time. These temporal variations must be accounted for in some fashion to maintain meaningful solutions. The primary goal of this study is to analyze the feasibility of using a sequential process noise filter for estimating geodynamic parameters over time from the Laser Geodynamics Satellite (LAGEOS) SLR data. This evaluation is achieved by first simulating a sequence of realistic LAGEOS laser ranging observations. These observations are generated using models with known temporal variations in several geodynamic parameters (along track drag and the J(sub 2), J(sub 3), J(sub 4), and J(sub 5) geopotential coefficients). A standard (non-stochastic) filter and a stochastic process noise filter are then utilized to estimate the model parameters from the simulated observations. The standard non-stochastic filter estimates these parameters as constants over consecutive fixed time intervals. Thus, the resulting solutions contain constant estimates of parameters that vary in time which limits the temporal resolution and accuracy of the solution. The stochastic process noise filter estimates these parameters as correlated process noise variables. As a result, the stochastic process noise filter has the potential to estimate the temporal variations more accurately since the constraint of estimating the parameters as constants is eliminated. A comparison of the temporal resolution of solutions obtained from standard sequential filtering methods and process noise sequential filtering methods shows that the accuracy is significantly improved using process noise. The results show that the positional accuracy of the orbit is improved as well. The temporal resolution of the resulting solutions are detailed, and conclusions drawn about the results. Benefits and drawbacks of using process noise filtering in this type of scenario are also identified.

Hartman, Brian Davis↗

NASA NTRS1996

Theory and ontology for sharing temporal knowledge

Using current technology, the sharing or re-using of knowledge-bases is very difficult, if not impossible. ARPA has correctly recognized the problem and funded a knowledge sharing initiative. One of the outcomes of this project is a formal language called Knowledge Interchange Format (KIF) for representing knowledge that could be translated into other languages. Capturing and representing design knowledge and reasoning with them have become very important for NASA who is a pioneer of innovative design of unique products. For upgrading an existing design for changing technology, needs, or requirements, it is essential to understand the design rationale, design choices, options and other relevant information associated with the design. Capturing such information and presenting them in the appropriate form are part of the ongoing Design Knowledge Capture project of NASA. The behavior of an object and various other aspects related to time are captured by the appropriate temporal knowledge. The captured design knowledge will be represented in such a way that various groups of NASA who are interested in various aspects of the design cycle should be able to access and use the design knowledge effectively. To facilitate knowledge sharing among these groups, one has to develop a very well defined ontology. Ontology is a specification of conceptualization. In the literature several specific domains were studied and some well defined ontologies were developed for such domains. However, very little, or no work has been done in the area of representing temporal knowledge to facilitate sharing. During the ASEE summer program, I have investigated several temporal models and have proposed a theory for time that is flexible to accommodate the time elements, such as, points and intervals, and is capable of handling the qualitative and quantitative temporal constraints. I have also proposed a primitive temporal ontology using which other relevant temporal ontologies can be built. I have investigated various issues of sharing knowledge and have proposed a formal framework for modeling the concept of knowledge sharing. This work may be implemented and tested in the software environment supplied by Knowledge Based System, Inc.

Loganantharaj, Rasiah↗

NASA NTRSDate not supplied

Temporal Interpolation of Geostationary Satellite Imagery with Task Specific Optical Flow

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the trade-offs to spatial, spectral and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary satellites have hemispheric coverage at 10-15 minute intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we apply a task specific optical flow approach to temporal up-sampling using deep convolutional neural networks. We apply this technique to 16-bands of GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full disk hemispheric snapshots of different spatial resolutions from 15 minutes to 1 minute. Experiments show the effectiveness of task specific optical flow and multi-scale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Lastly, we demonstrate strong performance in capturing variability during convective precipitation events.

Optical flow, temporal interpolation, geostationar↗

NASA NTRSDate not supplied

Seeing is Believing: Monitoring Future Time Temporal Logic

Runtime monitors for future-time unbounded temporal logics like RVLTL, LTL 3 and FLTL, have double-exponential (2^2^n) worst-case space complexity bounds in size of the input formula. The semantics of these logics require monitors to perform general satisfiability solving for LTL expressions, a well-studied problem whose computational complexity is NP-hard and PSPACE-complete. This paper introduces an unbounded future-time linear temporal logic defined over a lattice. We call our logic an incremental temporal logic as it can be viewed as incrementally constructing proofs about the trace. On this account, we view online runtime monitoring as a decision procedure for proofs systems about incrementally growing traces. We demonstrate that our incremental temporal logic allows monitor construction to void satisfiability solving while still soundly detecting when the property is violated in an online fashion. This enables asymptotic improvements in space complexity. As proof, we provide a procedure to construct monitors that utilize linear space and time in the size of the input formula, while remaining constant in the size of the input stream and suitable for online monitoring. We further demonstrate, through several examples, that our incremental temporal logic is straightforward to adopt and practical for runtime verification.

temporal logic↗

DOE OSTI2025

Visualizing Temporal Topic Embeddings with a Compass

—Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Cluster analysis↗

DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1987

The mechanisms of temporal inference

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

Fox, B. R.↗