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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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Unified Domain Knowledge Informed Machine Learning Model for CHF Prediction
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Plug and play: a general approach for the fusion of sensor and machine learning models
Regularized or Bayesian inversion has revolutionized our ability to reconstruct images from incomplete data.
Nanoparticle Catalytic Discovery using Machine Learning Models with Chemisorption Inputs
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Development of machine learning models for full field Reservoir Characterization
AGU 2021 Fall Meeting, Virtual, December 13-17, 2021.
An interpretable machine learning model for advancing terrestrial ecosystem predictions
We apply an interpretable Long Short-Term Memory (iLSTM) network for land-atmosphere carbon flux predictions based on time series observations of seven environmental variables. iLSTM enables interpretability of variable importance and variable-wise temporal importance to the prediction of targets by exploring internal network structures. The application results indicate that iLSTM not only improves prediction performance by capturing different dynamics of individual variables, but also reasonably interprets the different contribution of each variable to the target and its different temporal relevance to the target. This variable and temporal importance interpretation of iLSTM advances terrestrial ecosystem model development as well as our predictive understanding of the system.
MACHINE LEARNING MODELS OF BURST STRENGTH FOR DEFECT-FREE PIPELINES
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Application of Dimensionality Reduction in Machine Learning Modeling of CO2 Storage
In this study, we developed deep learning models that are capable of predicting spatio-temporal outputs of CO2 saturation, pressure, and brine production in a 3D saline storage reservoir over 30 years of continuous CO2 injection and a 50-year post-injection timeframe. To improve computational efficiency and maintain performance accuracy, the model framework involves ensembling multi-layer autoencoder networks that provide dimensionality reduction of geologic inputs with fully connected long short-term memory (LSTM) neural networks that generate time-series prediction. This study was presented as poster at the 2022 Carbon Management Project Review Meeting held in Pittsburgh, PA (August 15 – 19, 2022).
Deploying Isotope Identification Machine Learning Models Project #: 22-142 Year 1 of 3
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Deploying Isotope Identification Machine Learning Models
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Multiscale Optimization of Integrated Energy Systems Using Machine Learning Models for Market Interactions
The presentation was delivered at the 2022 American Institute of Chemical Engineers (AIChE) annual meeting.
Deploying Isotope Identification Machine Learning Models
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Reinforced Hierarchical Probabilistic Solar Forecasting Tool based on Dynamic Multi-model Machine Learning Phase I Final Technical Report
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Reduced dimensional representations of subsurface properties to enable computationally efficient and transferrable machine learning modeling
For presentation at the 2023 FECM/NETL Carbon Management Research Project Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
Defense of Supervised and Unsupervised Machine Learning Models
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Predicting Timescales in Dynamical Systems With Explainable Machine Learning Models
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Learning to Branch with Interpretable Machine Learning Models
The data consists of a slide deck that was presented at the INFORMS 2023 conference. The presentation summarizes our approach to learning how to branch and compares our approach to the popular solver SCIP and a state-of-the-art ML-based branching rule.