| Perspectives on the integration between first-principles and data-driven modeling W Bradley, J Kim, Z Kilwein, L Blakely, M Eydenberg, J Jalvin, C Laird, ... Computers & Chemical Engineering, 107898, 2022 | 55 | 2022 |
| A techno‐economic analysis framework for intensified modular systems V Gazzaneo, M Watson, CB Ramsayer, ZA Kilwein, V Alves, FV Lima Journal of Advanced Manufacturing and Processing 4 (3), e10115, 2022 | 6 | 2022 |
| AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models Z Kilwein, F Boukouvala, C Laird, A Castillo, L Blakely, M Eydenberg, ... Computer Aided Chemical Engineering 50, 919-925, 2021 | 5 | 2021 |
| Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow Z Kilwein, J Jalving, M Eydenberg, L Blakely, K Skolfield, C Laird, ... Energies 16 (16), 5913, 2023 | 2 | 2023 |
| Verification of Neural Network Surrogates J Haddad, M Bynum, M Eydenberg, L Blakely, Z Kilwein, F Boukouvala, ... Computer Aided Chemical Engineering 51, 583-588, 2022 | 2 | 2022 |
| Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow J Jalving, M Eydenberg, L Blakely, A Castillo, Z Kilwein, JK Skolfield, ... International Journal of Electrical Power & Energy Systems 157, 109741, 2024 | 1 | 2024 |
| Sampling-Based Vs. Surrogate-Based Techniques for Data-Driven Optimization: A Comparative Study of Adaptive Sampling and Hybrid-Modeling Approaches S Ravutla, Z Kilwein, F Boukouvala 2023 AIChE Annual Meeting, 2023 | | 2023 |
| IDEAL COMMUNITIES Z Kilwein, C Laird, NA Presenter, F Boukouvala, A Castillo, M Eydenberg, ... CEP, 2023 | | 2023 |
| Physics Informed Machine Learning for Feasibility Analysis Z Kilwein, M Eydenberg, L Blakely, F Boukouvala 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Resilience Enhancements through Deep Learning Yields. M Eydenberg, L Batsch-Smith, C Bice, L Blakely, M Bynum, F Boukouvala, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Physics-Informed Machine Learning Surrogates with Optimization-Based Guarantees: Applications to AC Power Flow. J Jalving, M Eydenberg, L Blakely, Z Kilwein, F Boukouvala, C Laird Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| Integration of Optimization and Machine Learning for Improving Electrical Grid Operation. C Laird, J Jalving, L Blakely, M Eydenberg, F Boukouvala, Z Kilwein Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| Online Graduate Certificate in Data Science for the Chemical Industry A Medford, F Boukouvala, M Grover, D Sholl, C Meredith, P Cheng, ... Chemical Engineering Education, 249-259, 2021 | | 2021 |
| Physics Informed Machine-Learning for Static Security Analysis of Optimal Power Flow Solutions Z Kilwein, L Blakely, A Castillo, M Eydenberg, CD Laird, F Boukouvala 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
| Modeling and Techno-Economic Analysis of a Modular Hydrogen Production Process Z Kilwein, V Gazzaneo, FV Lima 2018 AIChE Annual Meeting, 2018 | | 2018 |
| Physics-Informed Machine Learning with Optimization-Based Guarantees: Applications to Ac Power Flow MS Eydenberg, J Jalving, L Blakely, A Castillo, Z Kilwein, F Boukouvala, ... Available at SSRN 4245628, 0 | | |
