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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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Efficient sampling of constrained high-dimensional theoretical spaces with machine learning
Abstract Models of physics beyond the Standard Model often contain a large number of parameters. These form a high-dimensional space that is computationally intractable to fully explore. Experimental results project onto a subspace of parameters that are consistent with those observations, but mapping these constraints to the underlying parameters is also typically intractable. Instead, physicists often resort to scanning small subsets of the full parameter space and testing for experimental consistency. We propose an alternative approach that uses generative models to significantly improve the computational efficiency of sampling high-dimensional parameter spaces. To demonstrate this, we sample the constrained and phenomenological Minimal Supersymmetric Standard Models subject to the requirement that the sampled points are consistent with the measured Higgs boson mass. Our method achieves orders of magnitude improvements in sampling efficiency compared to a brute force search.
Machine Learning for Adaptive Model Refinement to Bridge Scales
This whitepaper is responsive to focal area (2) Predictive modeling through the use of AI techniques and AI-derived model components: the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Here we describe scale-aware ML models for adaptive model refinement that allow us to bridge the spatial and/or temporal scales in simulation models and observation data for capturing and predicting extreme water cycles.
Machine Learning for Surrogate Modeling of the Upper Ocean and Heat Exchange Between the Ocean and Atmosphere
This white paper primarily addresses Focal Area 2: “Predictive modeling through the use of AI techniques and AI-derived model components”. A secondary focus of the white paper is Focal Area 3: “Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods”.
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Accelerating Phase-field Predictions Via Surrogate Models Trained By Machine Learning Methods .
Abstract not provided.
Building a new generation of multiscale materials models with machine-learned interatomic potentials.
Abstract not provided.
Uncertainty Quantification of Machine Learning Interatomic Potential Models.
Abstract not provided.
Using Machine Learning for Predictive Modeling of Weather Impacts on Utility-scale Photovoltaic Systems.
Abstract not provided.
Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository.
Abstract not provided.
RaISE: A Framework to Characterize Surrogate Models in Scientific Machine Learning
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Applications of machine learning in the modeling and discovery of metal-(super)hydrides
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P219: Developing high-fidelity, machine-learning-based spectral models
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Developing Machine-Learning and Statistical Models for Western US Seasonal Precipitation Anomalies
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Scientific Machine Learning in Enhancing Model Predictability Through Model Form Error Quantification.
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Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models
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Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables
To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency.