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Local and Global Mass Balance of Physics-Informed Neural Network (PINN)
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Indirect Measurement of Underground Facility Expansion/Extent through the Multi-Modal Use of Cyber Metadata (Traceroutes) and Physics-Informed Models Applied to Pre-Post Digital Surface Mapping Project #: NLV-012-21 Year 2 of 3
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Automated Discovery of DOminaNt physics Informed Surrogates (ADDONIS) Framework.
Abstract not provided.
Adaptive Physics-Informed Machine Learning for Robust Beam Control and Diagnostics
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Certified data-driven physics-informed greedy auto-encoder simulator
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Local and Global Mass Conservation of Physics-informed Neural Network (PINN)
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ChemML: Physics-Informed AI/ML of Geochemical Datasets for Characterization, Parameterization, and Prediction of Contaminant Transport and Remediation Processes
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(U) Physics-Informed Deep Learning with Uncertainty Quantification for Weapons Radiography
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Indirect Measurement of Underground Facility Expansion/Extent through the Multi-Modal Use of Cyber Metadata (Traceroutes) and Physics-Informed Models Applied to Pre-Post Digital Surface Mapping
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Predicting Fluid Dynamics in Physical-informed Mesh-reduced Space
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Predicting Fluid Dynamics in Physical-informed Mesh-reduced Space
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Physics-Informed Multi-Output Surrogate Modeling of Fusion Simulations.
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Physics-Informed Deep Learning with Uncertainty Quantification for Weapons Radiography
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Acoustic scattering simulations via physics-informed neuralnetwork
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Physics-Informed Machine Learning for Void Fraction Prediction in Upward and Downward Two-Phase Flows
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Physics-informed machine learning of flow and transport problems
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Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters
The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.