Cospectral budget of turbulence explains the bulk properties of smooth pipe flow
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Katul, Gabriel G.↗
SEARCH · Engineering Papers
Results for “explainability”
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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Nuclear yield reduction in inertial confinement fusion exploding-pusher targets explained by fuel-pusher mixing through hybrid kinetic-fluid modeling
In inertial confinement fusion implosion experiments involving fast laser heating of glass capsules containing gaseous nuclear fuel, some nuclear measurements cannot be rendered by standard hydrodynamics numerical simulations. Here, the calculated values of the nuclear yield are found to be too high by more than a decade in some cases. The first kinetic simulations of the enclosed reacting gas have bridged only half that gap. In this Rapid Communication, using a hybrid ion kinetic-fluid numerical model which takes into account the kinetic interaction between the fuel and the pusher, we reach full agreement with the experimental yield data. We thus unambiguously demonstrate that the kinetic behavior of the system, resulting in both nonthermodynamic equilibrium of the ions comprising the fuel and strong pusher-fuel mixing, is the correct interpretation of the data.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Can Ab Initio Theory Explain the Phenomenon of Parity Inversion in Be 11 ?
The weakly bound exotic 11 Be nucleus, famous for its ground-state parity inversion and distinct n + 10 Be halo structure, is investigated from first principles using chiral two- and three-nucleon forces. An explicit treatment of continuum effects is found to be indispensable. We study the sensitivity of the 11 Be spectrum to the details of the three-nucleon force and demonstrate that only certain chiral interactions are capable of reproducing the parity inversion. With such interactions, the extremely large E 1 transition between the bound states is reproduced. We compare our photodisintegration calculations to conflicting experimental data and predict a distinct dip around the 3/2 – 1 resonance energy. Finally, we predict low-lying 3/2 + and 9/2 + resonances that are not or not sufficiently measured in experiments.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Dark Neutrino Portal to Explain MiniBooNE Excess
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Explaining the XENON1T Excess with Luminous Dark Matter
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Axion Emission Can Explain a New Hard X-Ray Excess from Nearby Isolated Neutron Stars
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79 ASTRONOMY AND ASTROPHYSICS↗
Explainable machine learning for hydrogen diffusion in metals and random binary alloys
Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.
36 MATERIALS SCIENCE↗
Cooperative Interactions between Surface Terminations Explain Photocatalytic Water Splitting Activity on Sr Ti O 3
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels
Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.
Gokhale, Swapna↗
How R Developers explain their Package Choice: A Survey
Background: Contemporary software development relies heavily on reusing already implemented functionality, usually in the form of packages. Aims: We aim to shed light on developers' preferences when selecting packages in R language. Method: To do that, we create and administer a survey to over 1000 developers who have added one of two common dataframe enhancement libraries in R to their projects: data.table or tidyr. We design a questionnaire using the Social Contagion Theory (SCT) following prior work on technology adoption and ensure that key dimensions affecting developer choice are considered. Results: Of the 1085 developers we contacted, 803 completed the survey asking them to prioritize various factors known to affect developer perceptions of package quality and to provide their background. Most developers self-identified as data scientists with two to five years of work experience. We found significant differences between the preferences of developers who chose data.table and tidyr. Surprisingly, package reputation based on easy-to-see measures, such as the number of stars on GitHub, was not an important factor for either group. Conclusions: Our findings demonstrate the inherently social nature of package adoption. They can help design future studies on how different populations of developers make decisions on which software packages to use in their projects. Finally, package developers and maintainers can benefit by better understanding the prime concerns of the users of their packages.
Malviya Thakur, Addi↗
Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation
Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.
Dunn, Jimmy C.↗
Explainable AI and Robustness-Based Test and Evaluation of Reinforcement Learning
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Raz, Ali K.↗
Building Trust in Earth Science Findings through Data Traceability and Results Explainability
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Olaya, Paula↗
Behavior of Single-Line-Ground Faults in Inverter-Based Resource Dominated Grids Explained
It has been observed by protection engineers that it is difficult for a protective relay to identify the faulted phase during a single-line-ground (SLG) fault in a power system with a high ingression of inverter-based resources (IBR) using currents (phase or sequence). Further studies using electromagnetic transient (EMT) simulation show that the initial operating conditions of the IBRs influence the response of phase currents during an SLG fault. In this letter, we conduct a quantitative analysis using sequence components. We find that the pre-fault condition determines the relative position of the current contributed by the grid versus that from the IBR, and further dictates which phase has the largest magnitude during an SLG condition. Finally, this finding is further verified by the EMT simulation results.
electromagnetic transient simulation↗
Situ: Identifying and Explaining Suspicious Behavior in Networks
Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major financial damages. Anomaly detection methods are beneficial for detecting new types of attacks and abnormal network activity, but such algorithms can be difficult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workflow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. Furthermore, we discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.
97 MATHEMATICS AND COMPUTING↗
SigTime: Learning and Visually Explaining Time Series Signatures
Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.
97 MATHEMATICS AND COMPUTING↗