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Results for “transfer learning.”
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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CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography
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Improving surrogate model accuracy for the LCLS-II injector frontend using convolutional neural networks and transfer learning
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Transfer Learning for Event Detection From PMU Measurements With Scarce Labels
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Probabilistic Approaches to Transfer Learning for Sparse and Noisy Data Environments.
Abstract not provided.
Coupling of NovelProbabilistic Transfer Learning Strategies and Autoencoders to Expedite Turbulent Combustion Modeling.
Abstract not provided.
Transfer Learning of Gaussian Processes to Capture Unmodeled Physics.
Abstract not provided.
Transfer Learning of Closure Terms and in Reduced Order Models of Chemically Reactive Flows.
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Transfer learning of Gaussian processes to capture unmodeled physics: application to control of nonlinear dynamical systems.
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Enhancing polynomial chaos expansion based surrogates through probabilistic transfer learning.
Abstract not provided.
Coupling of Novel Probabilistic Transfer Learning Strategies and Autoencoders to Expedite Turbulent Combustion Modeling.
Abstract not provided.
Transfer Learning of Reactor Power Models with Nonradiological Data
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Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning
The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.
Transfer learning for crack detection
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