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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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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Laboratory Experiments and Modeling for Interpreting Field Studies of Secondary Organic Aerosol Formation Using an Oxidation Flow Reactor

This grant was originally funded for deployment of a suite of aerosol instrumentation by our group in collaboration with other research groups and DOE/ARM to the Ganges Valley in India (GVAX) to study aerosols sources and processing. Much of the first year of this grant was focused on preparations for GVAX. That campaign was cancelled due to political reasons and with the consultation with our program manager, the research of this grant was refocused to study the applications of oxidation flow reactors (OFRs) for investigating secondary organic aerosol (SOA) formation and organic aerosol (OA) processing in the field and laboratory through a series of laboratory and modeling studies. We developed a gas-phase photochemical model of an OFR which was used to 1) explore the sensitivities of key output variables (e.g., OH exposure, O 3 , HO 2 /OH) to controlling factors (e.g., water vapor, external reactivity, UV irradiation), 2) develop simplified OH exposure estimation equations, 3) investigate under what conditions non-OH chemistry may be important, and 4) help guide design of future experiments to avoid conditions with undesired chemistry for a wide range of conditions applicable to the ambient, laboratory, and source studies. Uncertainties in the model were quantified and modeled OH exposure was compared to tracer decay measurements of OH exposure in the lab and field. Laboratory studies using OFRs were conducted to explore aerosol yields and composition from anthropogenic and biogenic VOC as well as crude oil evaporates. Various aspects of the modeling and laboratory results and tools were applied to interpretation of ambient and source measurements using OFR. Additionally, novel measurement methods were used to study gas/particle partitioning. The research conducted was highly successful and details of the key results are summarized in this report through narrative text, figures, and a complete list of publications acknowledging this grant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Interpreting New Data from the High Energy Frontier

This is the final technical report for DOE grant DE-SC0006389, "Interpreting New Data from the High Energy Frontier", describing research accomplishments by the PI in the field of theoretical high energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Validating nuclear models: electron data to interpret neutrino physics

Nuclear models in Monte Carlo event generators are crucial for interpreting neutrino scattering experiments, but are dicult to verify using only neutrino data due to incomplete data for some energy ranges. In particular, we seek to constrain the GENIE neutrino event generator's models. To overcome the uncertainties inherent to neutrino experiments, it is useful to take advantage of more controllable electron scattering experiments to evaluate current models and place constraints on future models. In this preliminary study on the feasibility of this strategy, we evaluate variables sensitive to the choice of nuclear model for the GENIE default model and data from the Jeerson Labs CLAS experiment. We nd some disagreement between them, likely explained by the the use of a dated default and background known to be remaining in the CLAS data that is not accounted for.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Characterizing and Interpreting the In Situ Strain Tensor During CO 2 Injection

Injecting fluid into a well creates an evolving strain tensor field in the rocks enveloping the well. Understanding the strain field has the potential to improve the effectiveness of CO 2 storage, but the strain tensor had never been measured during injection. The objective of this project was to evaluate how the strain tensor can be measured and interpreted to improve the assessment of geomechanical properties and advance an understanding of geomechanical processes that may present risks to CO 2 storage. The project consisted of three primary tasks related to 1) developing instruments for measuring the strain tensor with high precision; 2) developing methods for analyzing strain signals; and 3) demonstrating the approach at a CO 2 storage analog site.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Towards Trustworthy and Interpretable Deep Learning-assisted Ecohydrological Models

The transformational science question we plan to address is: How do we leverage in-situ observations and simulations from process-based ecohydrological model to construct interpretable and trustworthy deep learning (DL) models for improved reliability of prediction of quantities of interest (QoIs) under hydro-climatic extremes?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Verification of Data-Driven Models of Physical Phenomena using Interpretable Approximation

Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.

42 ENGINEERING↗
DOE OSTI2022

Interpretation of the Thermal Transitions of SEBS Triblock Copolymer

An interpretation of Differential Scanning Calorimetry (DSC) data gathered at Los Alamos National Laboratory (LANL) on various SEBS triblock copolymers is offered. A discussion of the complexity of these transitions as well as potential pitfalls that might be encountered when performing an analysis of a triblock copolymer is also given. Lastly, a look into the importance of SEBS triblock copolymers in the literature as well as current developments in their research is summarized.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Impact of Specificity on Human Interpretations of State Uncertainty

The goal of this project was test how different representations of state uncertainty impact human decision making. Across a series of experiments, we sought to answer fundamental questions about human cognitive biases and how they are impacted by visual and numerical information. The results of these experiments identify problems and pitfalls to avoid when for presenting algorithmic outputs that include state uncertainty to human decision makers. Our findings also point to important areas for future research that will enable system designers to minimize biases in human interpretation for the outputs of artificial intelligence, machine learning, and other advanced analytic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Application and Interpretation of Acumen Risk ® Schedule and Cost Risk Analysis

Schedule risk and cost risk analyses use statistical techniques to predict a level of confidence in meeting a program’s completion date and cost. This paper describes how to interpret key Schedule Risk Analysis (SRA) and Cost Risk Analysis (CRA) results to determine the likelihood that the program completion dates are met, how much management reserve (MR) is needed to provide the acceptable certainty of completion by a specific date, and the risks and activities that are most likely to delay or add cost to a project. The combined process is often called a Schedule/Cost Uncertainty and Risk Analysis or “SCURA.”

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Improved Data Interpretation through Identification of Time Series Periodicity Changes

Analysis and interpretation of time series data is easiest when the data values occur at uniform intervals in time, but actual data may have differing data sampling frequencies, such as monthly and daily readings. Applying data analysis techniques, such as smoothing, to such a data set may not give a representative result between time segments. The ability to automatically distinguish time segments of differing data frequency would provide a means for applying data analysis independently to each segment, though a suitable blending at segment boundaries would be required. A method for detecting frequency changes was developed and applied to Gaussian and median smoothing of hydraulic head data from groundwater wells at the U.S. Department of Energy Hanford Site in southeastern Washington state. The process identifies time segments of high-frequency (daily) or low-frequency (greater than daily) data using adjusted-bandwidth Gaussian kernel density estimation and a threshold value, which are further refined to address small blocks of low-frequency data within larger blocks of high-frequency data. User-selectable levels of smoothing are then applied independently to the time segments prior to combining the segment results for a single smoothed data set. This time segment identification approach provides effective low- and high-frequency data separation, which provides a method to apply data analysis independently to each time segment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Oppenheimer Interpretive Text Additions

Interpretive text for several items related to J. Robert Oppenheimer. This will be on display at the Bradbury Science Museum.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗
DOE OSTI2023

DeepDataProfiler: A Platform and Methodology for the Analysis and Interpretation of Neural Networks

The DeepDataProfiler is a methodology and framework for providing interpretability to trained neural networks. Its approach is to decompose a network into a weighted graph of neurons and synapses and link the components of the graph to human identifiable concepts. By identifying concepts important to the network and tracking the decision process employed by the network, the network becomes more transparent and less like a black box. Spurious decisions and poor generalization strategies can be identified and a measure of trustworthiness can be established.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Increased Interpretability for Model-Driven Deception: MARS LDRD Project

Machine learning has been proposed as a solution to several cybersecurity solutions and one of the most promising applications is for digital twins for intrusion detection and driving deceptive defense. However, machine learning techniques often result in a black-box function that is difficult for end users to interpret which for deception limits their ability to effectively define decoys. In this report, an approach to validate the equations learned are accurate is provided and demonstrated. Following, begins the process of addressing this issue for a model-driven deception technology that produces equations representing the physical process controlled by operation technology devices. This research was performed by applying subject matter expert context to machine learned models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Interpretable Net Load Forecasting Using Smooth Multiperiodic Features

We consider the problem of forecasting net load over a horizon such as one day, using a trailing window of past net load values as well as date and time. We focus on three variations on this problem: point forecasts, marginal quantile forecasts, and generating conditional samples of the future value. We propose a method that relies on linear regression using some custom engineered time-based features to capture multiple periodicities, such as daily, weekly, and seasonal, and their interactions. Our proposed models are readily interpretable, and rely on efficient and reliable convex optimization [1] to fit. We illustrate our method on four years worth of hourly net load data, comparing predictions made with various subsets of the features.

Ogut, Mehmet G↗
DOE OSTI2024

Measures of Holographic Correlation: Discovery, Interpretation, Application (Final Report)

The main goals of this project were twofold: (1) to find measures of quantum correlations that are amenable to interpretation in a holographic context, and use these measures to develop a more detailed understanding of holography itself and (2) To use insights developed in a holographic setting to better understand the theory of quantum information in more traditional (nonholographic) settings.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗