Publications

Li, J.D., Eydenberg, M.S., Yarritu, K.A., Shakamuri, M., Bridges, J.M., & Bridges, J.M. (2024). Streaming Analytics for Anomaly Detection in Large-Scale Data [Presentation]. 10.2172/2462941

Li, J.D., Eydenberg, M.S., Yarritu, K.A., Shakamuri, M., Bridges, J.M., & Bridges, J.M. (2024). Streaming Analytics for Enhancing Anomaly Detection in Satellite State-of-Health Telemetry [Conference Proceeding]. https://www.osti.gov/biblio/2430062

Jalving, J., Eydenberg, M.S., Blakely, L., Kilwein, Z., Skolfield, J.K., Castillo, A., Boukouvala, F., Laird, C., & Laird, C. (2024). Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow. International Journal of Electrical Power and Energy Systems, 157. https://doi.org/10.1016/j.ijepes.2023.109741

Kilwein, Z., Jalving, J., Blakely, L., Eydenberg, M.S., Skolfield, J.K., Laird, C., Boukouvala, F., & Boukouvala, F. (2023). Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow. Energies, 16(16). https://doi.org/10.3390/en16165913

Kilwein, Z., Eydenberg, M.S., Blakely, L., Skolfield, J.K., Boukouvala, F., & Boukouvala, F. (2022). Structured Physics Informed Neural Networks for Surrogate Based Feasibility [Conference Poster]. 10.2172/2006133

Rajamanickam, S., Eydenberg, M.S., Ho, Y., Liu, C., Zhang, L., Zhou, K., Sun, J.G.J., Chen, E., Deng, A., Wang, M., & Wang, M. (2022). Accelerating Selected DOE Machine Learning Workloads on SambaNova Systems [Conference Presenation]. 10.2172/2006163

Bradley, W., Kim, J., Kilwein, Z., Blakely, L., Eydenberg, M.S., Jalvin, J., Laird, C., Boukouvala, F., & Boukouvala, F. (2022). Perspectives on the integration between first-principles and data-driven modeling. Computers and Chemical Engineering, 166. 10.1016/j.compchemeng.2022.107898

Eydenberg, M.S., Batsch-Smith, L., Bice, C., Blakely, L., Bynum, M.L., Boukouvala, F., Castillo, A., Haddad, J., Hart, W.E., Jalving, J., Kilwein, Z., Laird, C., Skolfield, J.K., & Skolfield, J.K. (2022). Resilience Enhancements through Deep Learning Yields. 10.2172/1890044

Kilwein, Z., Jalving, J.H., Blakely, L., Eydenberg, M.S., Laird, C., Boukouvla, F., & Boukouvla, F. (2022). Deep Neural Networks as Surrogates for Intractable Constraints and Problem Dimension Reduction: SC ACOPF [Conference Presenation]. 10.2172/2001964

Haddad, J., Bynum, M.L., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Laird, C.D., Jalving, J., & Jalving, J. (2022). Verification of Neural Network Surrogates [Conference Presenation]. Computer Aided Chemical Engineering. 10.2172/2003604

Haddad, J., Bynum, M.L., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Carl, L., Jalving, J.H., & Jalving, J.H. (2021). Verification of Neural Network Surrogates [Conference Paper]. 10.1016/B978-0-323-95879-0.50098-9

Laird, C., Jalving, J.H., Blakely, L., Eydenberg, M.S., Boukouvala, F., Kilwein, Z., & Kilwein, Z. (2021). Integration of Optimization and Machine Learning for Improving Electrical Grid Operation [Conference Presenation]. 10.2172/1896366

Jalving, J.H., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Laird, C., & Laird, C. (2021). Physics-Informed Machine Learning Surrogates with Optimization-Based Guarantees: Applications to AC Power Flow [Conference Presenation]. 10.2172/1897922

Kilwein, Z., Boukouvala, F., Laird, C., Castillo, A., Blakely, L., Eydenberg, M.S., Jalving, J.H., Batsch-Smith, L., & Batsch-Smith, L. (2021). AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models [Conference Paper]. Computer Aided Chemical Engineering. 10.1016/B978-0-323-88506-5.50142-X

Eydenberg, M.S., Khanna, K., Custer, R., & Custer, R. (2020). Effects of Jacobian Matrix Regularization on the Detectability of Adversarial Samples. 10.2172/1763568