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A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA
Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.
Interpreting mixing relationships in energetic melts to estimate vapor contribution and composition
Heterogeneous, glassy debris can be produced by energetic events such as near surface nuclear explosions and asteroid impacts. In these events, the resultant debris comprises a rapidly-cooled mixture of the projectile or bomb material with proximate materials such as soil and anthropogenic structures. Glasses formed in these events preserve inter and/or intra sample compositional variation reflecting complex mixtures of the precursor materials or their chemically fractionated products. Understanding formation mechanisms and chemical processes relating to energetic glass formation based on study of the end products requires an objective method for calculating the precursor components that mixed together to produce the final product. Here, we compare a set of multivariate linear-least squares approaches to address the unmixing problem of heterogeneous glasses including classical least squares (CLS), principal component analysis (PCA) and two multivariate curve resolution – alternating least squares (MCR-ALS) approaches. We generate a synthetic data set representing spatially resolved, compositional data in a set of heterogeneously mixed glasses to understand model sensitivity to precursor abundance and composition. We explore the impact of incomplete knowledge of the system prior to the formation of the melts on the resulting confidence and accuracy of the solution (i.e., the number, composition, and abundances of the precursor materials). We conclude that a closure and equality constrained MCR-ALS approach succeeds in identifying unrecognized precursor components and is a suitable alternative to CLS approaches when the investigator has incomplete knowledge of the starting precursor chemical compositions. Finally, we demonstrate application of this MCR-ALS approach to measurements of Trinitite.
$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder
Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.
Interpretable A-posteriori error indication for graph neural network surrogate models
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Uncertainties in interpretation of high pressure spherical flame propagation rates due to thermal radiation
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Interpretable data-driven methods for subgrid-scale closure in LES for transcritical LOX/GCH4 combustion
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Interpreting oxygen vacancy migration mechanisms in oxides using the layered structure motif
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Machine learning for interpreting coherent X-ray speckle patterns
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, in this work, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.
Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials
Understanding the relationship between the evolution of microstructures of irradiated LiAlO2pellets and tritium diffusion, retention and release could improve predictions of tritium performance. Given expert-labeled segmented images of irradiated and unirradiated pellets, we trained Deep Convolutional Neural Networks to segment images into defect, grain, and boundary classes. Qualitative microstructural information was calculated from these segmented images to facilitate the comparison of unirradiated and irradiated pellets. We tested modifications to improve the sensitivity of the model, including incorporating meta-data into the model and utilizing uncertainty quantification. The predicted segmentation was similar to the expert-labeled segmentation for most methods of microstructural qualification, including pixel proportion, defect area, and defect density. Overall, the high performance metrics for the best models for both irradiated and unirradiated images shows that utilizing neural network models is a viable alternative to expert-labeled images.
Development of interpretable, data-driven plasticity models with symbolic regression
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CutLang: A Particle Physics Analysis Description Language and Runtime Interpreter
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Interpretation of Tracer Experiments on Inverted Five-spot Well-patterns within the Western Half of the Farnsworth Unit Oil Field
The Southwest Carbon Partnership (SWP), one of the U.S. Department of Energy (U.S. DOE) seven Regional Carbon Sequestration Partnerships, is currently working to demonstrate the utilization and storage of CO 2 in the Farnsworth Unit (FWU) Enhanced Oil Recovery (EOR) site under the final development phase of this U.S. DOE initiative. A component of the research is to use fluid tracers to understand the multifluid flow patterns that develop between injection and production wells via collected field data and supporting numerical reservoir models. The FWU, located in the Anadarko Basin, Ochiltree County, Texas, and being operated by Chaparral Energy, is a mature EOR water-flood field, which is currently being converted to a CO 2 flow, with inverted 5-spot patterns transitioning from pure water to alternating CO 2 and water floods (i.e., water alternating gas (WAG)) at an approximate rate of one every 6 to 10 months. The SWP tracer program is conducting a suite of tracer injections into the active 5-spot patterns at the FWU. Tracers have been selected to be nonreactive and either principally soluble in CO 2 (gas soluble) or water (aqueous soluble). In addition to characterizing the multifluid flow behaviour within reservoir, the gas and aqueous tracers have roles in detecting any leakage from the reservoir. A total of seven unique perfluorocarbon tracer (PFT) compounds make up the suite of gas soluble tracers and eight unique naphthalene sulfonate tracer (NPT) compounds comprise the aqueous soluble tracers. Lastly, all selected tracers are significantly detectable below the parts per billion concentrations, allowing for high resolution for the inter-well tests at relatively low injection volumes.
Interpreting best available technologies more flexibly: A policy perspective for municipal wastewater management in India and other developing countries
Inadequate sanitation is amongst the causes of escalating pollution problems in developing countries, as municipal wastewater treatment systems remove only a fraction of pollutants that could be removed with best available technologies (BAT). Although BAT is a proven instrument of environmental policies, its potential for municipalities remains largely unused in developing countries. In order to ease its implementation, the paper developed a simplified assessment approach towards identifying an approximating of BAT in terms of a “flexible BAT” (FlexiBAT), which is based on the identification of national reference plants assessed with respect to pollutant removal (environmental impact, health impact), costs (economic viability, affordability) and social acceptability. The concept was tested for 58 case studies in India, where none of the technologies passed all tests for FlexiBAT. Therefore, there is a need to improve or develop better and more innovative technologies. Amongst the most promising ones, membrane bioreactors provided good physical water quality, but costs were high, while for moving bed biofilm reactors costs were low, but water quality was insufficient. Conventional onsite systems require separate consideration. In order to ease the identification of FlexiBAT, a national environmental information system with data from the regular monitoring of existing plants would be needed.
Simulations and Interpretation of Three-Electrode Cyclic Voltammograms of Pseudocapacitive Electrodes
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Interpretable multi-source data fusion through Latent Variable Gaussian Process
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