Models and data: a critical relationship for constraining uncertainty in ice sheet model projections
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Larour, Eric↗
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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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Physics-Informed Data-driven Modeling of Supersonic Retropropulsion
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Supersonic Retropropulsion↗
Constellation Program Design Specification for Natural Environments
The Design Specification for Natural Environments (DSNE) completes environment-related requirements from architecture, system level, and lower tier documents by specifying the ranges of environmental conditions that must be accounted for by the design of all Constellation Program (CxP) elements. As such, it forms a part of those specifications. It has been pulled out into a separate document to assure clarity and consistency and to prevent the requirements documents from becoming cluttered with extensive amounts of technical material. It is based on the Constellation Design Reference Missions and Operational Concepts Document and the models, data, and environment descriptions in the Natural Environments Definition for Design (NEDD). The NEDD provides additional detailed environment data and model descriptions to support analytical studies for Constellation (Cx) systems.
Constellation Program↗
The Prompt Fission Neutron Spectrum: From Experiment to the Evaluated Data and its Impact on Critical Assemblies
After a brief introduction concerning nuclear data, prompt fission neutron spectrum (PFNS) evaluations and the limited PFNS covariance data in the ENDF/B-VII library, and the important fact that cross section uncertainties ~ PFNS uncertainties, the author presents background information on the PFNS (experimental data, theoretical models, data evaluation, uncertainty quantification) and discusses the impact on certain well-known critical assemblies with regard to integral quantities, sensitivity analysis, and uncertainty propagation. He sketches recent and ongoing research and concludes with some final thoughts.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Conditions Databases at FNAL
Conditions databases is an important class of database applications where the database is used to record the state of a set of quantities as a function of observation time. Conditions databases are used in Hight Energy Physics to record the state of the detector apparatus during data taking, and then to use the data during the event reconstruction and analysis phases. At FNAL, we have a set of 3 different conditions database products, Minerva Conditions DB, ConDB and UConDB, which cover the whole range of the use cases presented by the FNAL experiments. These products have common features such as conditions data representation model, data version control, ability to restore the database to a previous state, scalable web service data access interface. In the paper, we will present the common features of the products, common solutions used to build them and also the differences between the products and their target use cases.
Mandrichenko, Igor V.↗
Data-Driven Modeling and Correction of Vehicle Dynamics
We develop a data-driven framework for learning and correcting nonautonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, nonautonomous dynamics are governed by time-dependent control inputs, which pose challenges in learning predictive models directly from temporal snapshot data. To address these, we reformulate the vehicle dynamics via a local parameterization of the time-dependent inputs, yielding a modified system composed ofa sequence of local parametric dynamical systems. Here, we approximate these parametric systems using two complementary approaches. First, we employ the dimension reduction and interpolation in parameter space (DRIPS) methodology to construct efficient linear surrogate models, equipped with lifted observable spaces and manifold-based operator interpolation. This enables data-efficient learning of vehicle models whose dynamics admit accurate linear representations in the lifted spaces. Second, for more strongly nonlinear systems, we employ flow map learning (FML), a deep neural network (DNN) approach that approximates the parametric evolution map without requiring special treatment of nonlinearities. We further extend FML with a transfer-learning-based model correction procedure, enabling the correction of misspecified prior models using only a sparse set of high-fidelity or experimental measurements, without assuming a prescribed form for the correction term. Through a suite of numerical experiments on unicycle, simplified bicycle, and slip-based bicycle models, we demonstrate that DRIPS offers robust and highly data-efficient learning of nonautonomous vehicle dynamics, while FML provides expressive nonlinear modeling and effective correction of model-form errors under severe data scarcity.
data-driven modeling↗
Influence of data uncertainty on cold season threshold-based climate indices
Climate indices are used to reduce the complex climate system and its changes to simple measures. The data basis – whether observational data or climate model data – to which the climate indices are applied, is usually subject to uncertainties. For threshold-based climate indices, the data uncertainty influences the threshold value, and, hence, the uncertainty can influence the values for the climate index. What the actual impacts of these uncertainties are on threshold-based climate indices is examined in this paper. The focus is not only on the climate model uncertainty, but also on the observational data uncertainty. The general sensitivity of each of the chosen climate indices to arbitrary changes in the threshold is studied. This shows a higher sensitivity of indices assessing extremes (ice days, heavy precipitation days) to changes in the threshold than indices that integrate a quantity over a given time interval (coldsum, consecutive days). For assessing an ensemble of climate model data with respect to their ability to reproduce the index values for current climate, the reference data uncertainty is applied to the chosen threshold-based climate indices by changing their threshold value by its corresponding uncertainty. It is shown that the climate model uncertainty can be within the range of the reference data uncertainty. When using threshold-based climate indices to assess changes in future climate periods, uncertainties should always be taken into account and ideally corrected in an appropriate way. This is especially important for indices that assess extremes.
54 ENVIRONMENTAL SCIENCES↗
A survey of commercial object-oriented database management systems
The object-oriented data model is the culmination of over thirty years of database research. Initially, database research focused on the need to provide information in a consistent and efficient manner to the business community. Early data models such as the hierarchical model and the network model met the goal of consistent and efficient access to data and were substantial improvements over simple file mechanisms for storing and accessing data. However, these models required highly skilled programmers to provide access to the data. Consequently, in the early 70's E.F. Codd, an IBM research computer scientists, proposed a new data model based on the simple mathematical notion of the relation. This model is known as the Relational Model. In the relational model, data is represented in flat tables (or relations) which have no physical or internal links between them. The simplicity of this model fostered the development of powerful but relatively simple query languages that now made data directly accessible to the general database user. Except for large, multi-user database systems, a database professional was in general no longer necessary. Database professionals found that traditional data in the form of character data, dates, and numeric data were easily represented and managed via the relational model. Commercial relational database management systems proliferated and performance of relational databases improved dramatically. However, there was a growing community of potential database users whose needs were not met by the relational model. These users needed to store data with data types not available in the relational model and who required a far richer modelling environment than that provided by the relational model. Indeed, the complexity of the objects to be represented in the model mandated a new approach to database technology. The Object-Oriented Model was the result.
Atkins, John↗
Model America
Data set including 17 fields of information for 141,506,665 U.S. buildings. Advantages of this dataset include: Resolution - actionable data and models for individual U.S. buildings; Flexibility - ability to modify data, models, and analysis for any simulation-informed use case; Scalability - dynamic archetypes can be used as representative models for any geography of interest; Accuracy - error rates for total energy use at city-scale of 1-5%
New, JoshuaR↗
Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models
Uncertainty visualization is a key component in translating important insights from ensemble simulation data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models trained on ensemble data, we can substitute computationally expensive simulations, which allows users to interact with more aspects of data spaces than ever before. However, the use of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.
ensemble data↗
Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling
The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.
machine learning↗
Interval Predictor Models for Data with Measurement Uncertainty
An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.
Lacerda, Marcio J.↗
SCATHA SSPM charging response: NASCAP predictions compared with data
Models for the satellite surface potential monitor (SSPM) units constructed in the NASCAP code and the results of comparing predictions to surface voltage and baseplate current data are reported. Several peculiarities in the test data are noted. Preliminary results from space simulations of a SCATHA model with environments representative of the day 87, 1979, eclipse injection event are presented, and their implications for predicting space response are discussed.
Purvis, C. K.↗
Data-Driven Modeling of Spatially Explicit Air Pollutant Inventories for Agricultural and Forestry Feedstock Production
As biomass feedstock production expands, air pollutants generated from feedstock supply chain activities may impact air quality and human health. The National Renewable Energy Laboratory's Feedstock Production Emissions to Air Model (FPEAM) generates county-scale inventories of direct air pollutant emissions produced by feedstock supply chain activities. FPEAM can also be used to generate local, temporally resolved inventories for input into air quality models. This presentation provides an overview of FPEAM functionality and discusses several recent use cases prior to FPEAM's scheduled public release in September 2019. FPEAM is a modular, data-driven model. Each module contains calculations for an activity type, except for the feedstock transportation and on-farm equipment operation modules which connect to the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) and Nonroad models, respectively. The data-driven aspect of FPEAM means that it is the input data provided for a scenario rather than the model itself that determines the pollutants and supply chain activities included in the scenario scope. Modules within FPEAM can also be turned on or off manually to adjust the scenario scope. Default input data packaged with the FPEAM code base can be used to calculate inventories of volatile organic compounds, particulate matter, ammonia, nitrogen oxides, sulfur oxides, and carbon monoxide generated by agricultural and forestry equipment operation, application of nitrogenous fertilizers, herbicide and insecticide, and feedstock transportation. Custom, user-developed data can be used in place of the default data to analyze scenarios with additional or alternate feedstocks, pollutants, and supply chain activities.
09 BIOMASS FUELS↗
Influence of initial conditions on data-driven model identification and information entropy for ideal mhd problems
Data-driven methods of model identification are able to discern governing dynamics of a system from data. Such methods are well suited to help us learn about systems with unpredictable evolution or systems with ambiguous governing dynamics given our current understanding. Many plasma problems of interest fall into these categories as there are a wide range of models that exist, however each model is only useful in a certain regime and often limited by computational complexity. To ensure data-driven methods align with theory, they must be consistent and predictable when acting on data whose governing dynamics are known. Weak Sparse Identification of Nonlinear Dynamics (WSINDy) is a recently developed data-driven method that has shown promise in learning governing dynamics from data with high noise levels [1]. This work examines how WSINDy acts on ideal MHD test problems as the initial conditions are varied and specifies limiting requirements for successful equation identification. Furthermore, it is hard to recover the governing dynamics from data that emphasize a single dominant behavior. In these low information cases, Shannon information entropy is able to pick up on the redundancies in the data that affect recoverability.
97 MATHEMATICS AND COMPUTING↗
Data-driven model construction for industrial asset decision boundary classification
In some embodiments, a system model construction platform may receive, from a system node data store, system node data associated with an industrial asset. The system model construction platform may automatically construct a data-driven, dynamic system model for the industrial asset based on the received system node data. A synthetic attack platform may then inject at least one synthetic attack into the data-driven, dynamic system model to create, for each of a plurality of monitoring nodes, a series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. The synthetic attack platform may store, in a synthetic attack space data source, the series of synthetic attack monitoring node values over time that represent simulated attacked operation of the industrial asset. This information may then be used, for example, along with normal operational data to construct a threat detection model for the industrial asset.
97 MATHEMATICS AND COMPUTING↗
Object storage model for CMS data
In CMS, data access and management is organized around the data-tier model: a static definition of what subset of event information is available in a particular dataset, realized as a collection of files. In previous work, we have proposed a novel data management model that obviates the need for data tiers by exploding files into individual event data product objects. In this work, we estimate the potential savings in data volume based on user analysis patterns.
Smith, Nick↗
Instruction manual model 600F, data transmission test set
Information necessary for the operation and maintenance of the Model 600F Data Transmission Test Set is presented. A description is contained of the physical and functional characteristics; pertinent installation data; instructions for operating the equipment; general and detailed principles of operation; preventive and corrective maintenance procedures; and block, logic, and component layout diagrams of the equipment and its major component assemblies.
Source record↗