Brenda Pracheil Explains Otolith Microchemistry
Brenda Pracheil explains otolith microchemistry.
59 BASIC BIOLOGICAL SCIENCES↗
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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2018 Fermilab Physics Slam: Explaining neutrino oscillations
For his 2018 Physics Slam presentation at Fermilab, Northwestern University scientist André de Gouvêa took on one of the most fascinating particles in physics: the neutrino. In 10 minutes, he explained—with the help of a few props—what neutrinos are and how physicists discovered that these particles can transform into one other, a phenomenon known as neutrino oscillation. At the end of the evening, the audience declared him to be the winner of the 2018 Physics Slam. Curious about neutrinos? Learn more about these particles at All Things Neutrino: http://neutrinos.fnal.gov
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Explaining Missing Data in Graphs: A Constraint-based Approach
Abstract: This paper introduces a constraint-based approach to clarify missing values in graphs. Our method capitalizes on a set S of graph data constraints. An explanation is a sequence of operational enforcement of S towards the recovery of interested yet missing data (e.g., attribute values, edges). We show that constraint-based approach helps us to understand not only why a value is missing, but also how to recover the missing value. We study S-explanation problem, which is to compute the optimal explanations with guarantees on the informativeness and conciseness. We show the problem is in ?P^2 for established graph data constraints such as graph keys and graph association rules. We develop an efficient bidirectional algorithm to compute optimal explanations, without enforcing S on the entire graph. We also show our algorithm can be easily extended to support graph refinement within limited time, and to explain missing answers. Using real-world graphs, we experimentally verify the effectiveness and efficiency of our algorithms.
Data Analytics↗
Explained: Fundamentals of Power Grid Reliability and Clean Electricity
Maintaining reliability of the bulk power system, which supplies and transmits electricity, is a critical priority for electric grid planners, operators, and regulators. As we move toward a cleaner electricity system with more technologies like wind, solar, and battery storage, the way in which we plan for and achieve reliability will change. This fact sheet provides additional technical background to the topics covered in three other reliability explainers on how we measure, enforce, and plan for reliable systems with more clean electricity.
POWER TRANSMISSION AND DISTRIBUTION↗
Improving Machine Learning Explainability with a Graphical User Interface
Improving machine learning explainability with a graphical user interface
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning
To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.
97 MATHEMATICS AND COMPUTING↗
Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation
Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Data-driven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations.
60 APPLIED LIFE SCIENCES↗
Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method
Not Available
13 HYDRO ENERGY↗
Investigating explainable transfer learning for battery lifetime prediction under state transitions
Battery lifetime prediction at early cycles is crucial for researchers and manufacturers to examine product quality and promote technology development. Machine learning has been widely utilized to construct data-driven solutions for high-accuracy predictions. However, the internal mechanisms of batteries are sensitive to many factors, such as charging/discharging protocols, manufacturing/storage conditions, and usage patterns. These factors will induce state transitions, thereby decreasing the prediction accuracy of data-driven approaches. Transfer learning is a promising technique that overcomes this difficulty and achieves accurate predictions by jointly utilizing information from various sources. Hence, we develop two transfer learning methods, Bayesian Model Fusion and Weighted Orthogonal Matching Pursuit, to strategically combine prior knowledge with limited information from the target dataset to achieve superior prediction performance. From our results, our transfer learning methods reduce root-mean-squared error by 41% through adapting to the target domain. Furthermore, the transfer learning strategies identify the variations of impactful features across different sets of batteries and therefore disentangle the battery degradation mechanisms and the root cause of state transitions from the perspective of data mining. These findings suggest that the transfer learning strategies proposed in our work are capable of acquiring knowledge across multiple data sources for solving specialized issues.
25 ENERGY STORAGE↗
Explainable machine-learning tools for predictive maintenance of circulating water systems in nuclear power plants
Not Available
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
An Explainable Classification Framework for Determining and Understanding the Suitability of Solvent Extraction for Bioproduct Recovery
Lignocellulosic biomass is an abundant feedstock for producing sustainable fuels and chemicals. However, a key challenge in most biomass utilization strategies is the recovery of products from a dilute, typically aqueous, phase. In this respect, liquid–liquid extraction, which relies on a solvent to transfer a product of interest from one liquid phase to another (solvent-rich) phase, is a technology that can reduce the energy requirements for product recovery. To reduce solvent consumption, liquid–liquid extraction needs to be combined with another separation method (e.g., distillation) to recycle the solvent. Despite the research on solvent extraction, there are limited system-wide methods that allow us to determine when extraction is well suited to carry out a specific separation. Accordingly, we present a classification framework to predict whether extraction, coupled with distillation, is feasible and more economical than distillation. Our framework is based on features such as feed composition, liquid–liquid equilibrium constants, relative volatilities, and solvent price, and leads to trained classifiers that show good prediction accuracy. We further study how specific features influence the suitability of extraction. Furthermore, to showcase the applicability of the framework, we use it to analyze the separation of acetic acid from water.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach
The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems while allowing for underlying complex subsystem level interactions. Particularly, we present the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. Here, we detail a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64,000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.
97 MATHEMATICS AND COMPUTING↗
Invariant soil water potential at zero microbial respiration explained by hydrological discontinuity in dry soils
We report that soil microbial respiration rates decrease with soil drying, ceasing below water potentials around -15 MPa. A proposed mechanism for this pattern is that under dry conditions, microbes are substrate limited because solute diffusivity is halted due to breaking of water film continuity. However, pore connectivity estimated from hydraulic conductivity and solute diffusivity (at Darcy's scale) is typically interrupted at much less negative water potentials than microbial respiration (-0.1 to -1 MPa). It is hypothesized here that the more negative respiration thresholds than at the Darcy's scale emerge because microbial activity is restricted to microscale soil patches that retain some hydrological connectivity even when it is lost at the macroscale. This hypothesis is explored using results from percolation theory and meta-analyses of respiration-water potential curves and hydrological percolation points. Lastly, when reducing the spatial scale from macroscale to microscale, hydrological and respiration thresholds become consistent, supporting the proposed hypothesis.
58 GEOSCIENCES↗
Atmospheric rivers do not explain UK summer extreme rainfall: ARS AND UK EXTREME RAINFALL
Extreme rainfall events continue to be one of thelargest natural hazards in the UK. In winter,heavy precipitation and floods have been linked with intense moisture transport events associated withatmospheric rivers (ARs), yet no large-scale atmospheric precursors have been linked to summer flooding inthe UK. This study investigates the link between ARs and extreme rainfall from two perspectives: (1) Givenan extreme rainfall event, is there an associated AR?(2) Given an AR, is there an associated extreme rainfallevent? We identify extreme rainfall events using theUK Met Office daily rain gauge data set and link theseto ARs using two different horizontal resolution atmospheric data sets (ERA-Interim and Twentieth CenturyReanalysis). The results show that less than 35% of winter ARs and less than 15% of summer ARs areassociated with an extreme rainfall event. Consistent with previous studies, at least 50% of extreme winterrainfall events are associated with an AR. However, less than 20% of the identified summer extreme rainfallevents are associated with an AR. The dependence of the water vapor transport intensity threshold used todefine an AR on the years included in the study, and on the length of the season, is also examined. Includinga longer period (1900–2012) compared to previous studies (1979–2005) reduces the water vapor transportintensity threshold used to define an AR.
54 ENVIRONMENTAL SCIENCES↗
Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data
Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.
Barakati, Kamyar [University of Tennessee, Knoxvil↗
Suppressed Lone Pair Electrons Explain Unconventional Rise of Lattice Thermal Conductivity in Defective Crystalline Solids
Manipulating thermal properties of materials can be interpreted as the control of how vibrations of atoms (known as phonons) scatter in a crystal lattice. Compared to a perfect crystal, crystalline solids with defects are expected to have shorter phonon mean free paths caused by point defect scattering, leading to lower lattice thermal conductivities than those without defects. While this is true in many cases, alloying can increase the phonon mean free path in the Cd-doped AgSnSbSe 3 system to increase the lattice thermal conductivity from 0.65 to 1.05 W m –1 K –1 by replacing 18% of the Sb sites with Cd. It is found that the presence of lone pair electrons leads to the off-centering of cations from the centrosymmetric position of a cubic lattice. X-ray pair distribution function analysis reveals that this structural distortion is relieved when the electronic configuration of the dopant element cannot produce lone pair electrons. Furthermore, a decrease in the Grüneisen parameter with doping is experimentally confirmed, establishing a relationship between the stereochemical activity of lone pair electrons and the lattice anharmonicity. The observed “harmonic” behavior with doping suggests that lone pair electrons must be preserved to effectively suppress phonon transport in these systems.
36 MATERIALS SCIENCE↗
Explaining the Size Dependence in Platinum-Nanoparticle-Catalyzed Hydrogenation Reactions
Hydrogenation reactions are industrially important reactions that typically require unfavorably high H 2 pressure and temperature for many functional groups. Herein we reveal surprisingly strong size-dependent activity of Pt nanoparticles (PtNPs) in catalyzing this reaction. Based on unambiguous spectral analyses, the size effect has been rationalized by the size-dependent d-band electron structure of the PtNPs. This understanding enables production of a catalyst with size of 1.2 nm, which shows a sixfold increase in turnover frequency and 28-fold increase in mass activity in the regioselective hydrogenation of quinoline, compared with PtNPs of 5.3 nm, allowing the reaction to proceed under ambient conditions with unprecedentedly high reaction rates. Finally, the size effect and the synthesis strategy developed herein may provide a general methodology in the design of metal-nanoparticle-based catalysts for a broad range of organic syntheses.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Defined and Flexible Pocket Explains Aryl Substrate Promiscuity of the Cahuitamycin Starter Unit-Activating Enzyme CahJ
Abstract not provided
Tripathi, Ashootosh↗