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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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Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
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Forecasting Individual Patient Response to Radiation Therapy in Head and Neck Cancer With a Dynamic Carrying Capacity Model
To model and predict individual patient responses to radiation therapy.
Stabilized neural ordinary differential equations for long-time forecasting of dynamical systems
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Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model
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Accuracy of wind energy forecasts in Great Britain and prospects for improvement
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Correcting physics-based global tide and storm water level forecasts with the temporal fusion transformer
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Towards real-time forecasting of natural gas production by harnessing graph theory for stochastic discrete fracture networks
In this work, we compare hydrocarbon production curves obtained from a graph-based reduced-order model with the high-fidelity Discrete Fracture Network (DFN) predictions for a fracture network created using data from a real shale site. We observe that the bounds for the high fidelity DFN model lie within the bounds for the reduced order model, implying that the reduced-order model provides a conservative estimate. Moreover, we found that except for first-passage times and late arriving mass, the production curves from the reduced-order model predict transport accurately. However, it is to be noted that the results are inspite of trading a three-dimensional geometry for a reduced system in the form of a graph, one that is 500–1000 times faster in terms of computational efficiency (for this particular application). In addition, we also compare the production curves for large drawdown and small drawdown using our graph approach. The reduced-order model is successful in showing that the long term productivity is higher in case of small drawdown although the initial productivity is higher for large drawdown. Thus, this reduced-order model offers great potential in uncertainty quantification for production, as well as in providing operators with information to make real-time decisions for optimal production.
Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification
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Optimized NMME Climate Forecasts for Rangeland Management Applications in the Western United States
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Exploring the physical limits of hydraulic fracture caging to forecast its feasibility for geothermal power generation
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Spatial adoption forecast methodology for photovoltaic systems throughout a city
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SolarNet: A sky image-based deep convolutional neural network for intra-hour solar forecasting
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Pursuing the impossible (?) dream: Incorporating attitudes into practice-ready travel demand forecasting models
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