Machine learning with gradient-based optimization of nuclear waste vitrification with uncertainties and constraints
Gekko is an optimization suite in Python that solves optimization problems involving mixed-
integer, nonlinear, and differential equations. The purpose of this study is to integrate
common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR),
support vector regression (SVR), and artificial neural network (ANN) models into Gekko to
solve data based optimization problems. Uncertainty quantification (UQ) is used alongside
ML for better decision making. These methods include ensemble methods, model-specific …
integer, nonlinear, and differential equations. The purpose of this study is to integrate
common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR),
support vector regression (SVR), and artificial neural network (ANN) models into Gekko to
solve data based optimization problems. Uncertainty quantification (UQ) is used alongside
ML for better decision making. These methods include ensemble methods, model-specific …
[PDF][PDF] Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints. Processes 2022, 10, 2365
Gekko is an optimization suite in Python that solves optimization problems involving mixed-
integer, nonlinear, and differential equations. The purpose of this study is to integrate
common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR),
support vector regression (SVR), and artificial neural network (ANN) models into Gekko to
solve data based optimization problems. Uncertainty quantification (UQ) is used alongside
ML for better decision making. These methods include ensemble methods, model-specific …
integer, nonlinear, and differential equations. The purpose of this study is to integrate
common Machine Learning (ML) algorithms such as Gaussian Process Regression (GPR),
support vector regression (SVR), and artificial neural network (ANN) models into Gekko to
solve data based optimization problems. Uncertainty quantification (UQ) is used alongside
ML for better decision making. These methods include ensemble methods, model-specific …
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