Data Driven PSE @ GT ChBE Boukouvala Research Lab

Publications

Here, you’ll find our latest contributions to journals, conferences and book chapters, showcasing our advancements in the areas of data-driven optimization, hybrid modeling, process-informed machine learning, plastics recycling, carbon capture, resilient grid operations and more. Notice that we are committed to collaboration and the dissemination of impactful research. Feel free to explore our work and reach out for discussions or potential collaborations!

Highlighted

Perspectives on the integration between first-principles and data-driven modeling
Perspectives on the integration between first-principles and data-driven modeling
William Bradley, Jinhyeun Kim, Zachary Kilwein, Logan Blakely, Michael Eydenberg, Jordan Jalvin, Carl Laird, Fani Boukouvala
Computers & Chemical Engineering   ·   01 Oct 2022   ·   doi:10.1016/j.compchemeng.2022.107898
Enabling global interpolation, derivative estimation and model identification from sparse multi-experiment time series data via neural ODEs
Enabling global interpolation, derivative estimation and model identification from sparse multi-experiment time series data via neural ODEs
William Bradley, Ron Volkovinsky, Fani Boukouvala
Engineering Applications of Artificial Intelligence   ·   01 Apr 2024   ·   doi:10.1016/j.engappai.2023.107611
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
Zachary Kilwein, Jordan Jalving, Michael Eydenberg, Logan Blakely, Kyle Skolfield, Carl Laird, Fani Boukouvala
Energies   ·   10 Aug 2023   ·   doi:10.3390/en16165913

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