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Full-Text Articles in Engineering
Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren
Machine Learning With Gradient-Based Optimization Of Nuclear Waste Vitrification With Uncertainties And Constraints, Lagrande Gunnell, Kyle Manwaring, Xiaonan Lu, Jacob Reynolds, John Vienna, John Hedengren
Faculty Publications
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 methods, conformal predictions, and the delta method. An optimization problem involving nuclear waste vitrification is presented to demonstrate the benefit of ML in this field. ML models are compared …
Machine Learning Prediction Of Glass Transition Temperature Of Conjugated Polymers From Chemical Structure, Amirhadi Alesadi, Zhiqiang Cao, Zhaofan Li, Song Zhang, Haoyu Zhao, Xiaodan Gu, Wenjie Xia
Machine Learning Prediction Of Glass Transition Temperature Of Conjugated Polymers From Chemical Structure, Amirhadi Alesadi, Zhiqiang Cao, Zhaofan Li, Song Zhang, Haoyu Zhao, Xiaodan Gu, Wenjie Xia
Faculty Publications
Predicting the glass transition temperature (Tg) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict Tg of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 Tg data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for Tg. MD simulations further unravel the fundamental roles …
Large-Scale Reality Modeling Of A University Campus Using Combined Uav And Terrestrial Photogrammetry For Historical Preservation And Practical Use, Bryce Berrett, Cory Vernon, Haley Beckstrand, Madi Pollei, Kaleb Markert, Kevin Franke, John Hedengren
Large-Scale Reality Modeling Of A University Campus Using Combined Uav And Terrestrial Photogrammetry For Historical Preservation And Practical Use, Bryce Berrett, Cory Vernon, Haley Beckstrand, Madi Pollei, Kaleb Markert, Kevin Franke, John Hedengren
Faculty Publications
Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic …
Intelligent Machine Learning: Tailor-Making Macromolecules, Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, Krzysztof Matyjaszewski
Intelligent Machine Learning: Tailor-Making Macromolecules, Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, Krzysztof Matyjaszewski
Faculty Publications
Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and …
Intelligent Machine Learning: Tailor-Making Macromolecules, Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, Krzysztof Matyjaszewski
Intelligent Machine Learning: Tailor-Making Macromolecules, Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, Krzysztof Matyjaszewski
Faculty Publications
Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and …
Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren
Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren
Faculty Publications
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and …