Research

Sponsor(s):  The Department of Energy (DOE)

Principal Investigators: Wach, P. (VT NSI), Co-PIs: Topcu T.G., Esser, K., & Beling P.

Description: The objective of this research is to identify, investigate, analyze, and optimize social and technical factors that act as barriers or levers to effective and efficient digital engineering transformation within NNSA. Our hypothesis is that effective and efficient digital transformation requires alignment between data, people, processes, tools, and products.

Sponsor(s):  US Naval Surface Warfare Center ‑ Dahlgren Division

Principal Investigators: Gilbert, J. (VT NSI), Schroeder, K. (VT NSI), : Topcu T.G, Brown, A. (VT AOE) , & Kerr, G (VT NSI) 

Description: The objective of this proposal is to advance the state of the art in integrated top-side design, more specifically layout arrangement, through time efficient modeling and simulation techniques. The project has multiple interrelated thrusts. The aspects that pertain to STSELab aim to-investigate how digital connectivity could enable integrated analytical tools and multi-modal representations of mission constraints to identify and evaluate design alternatives from a mission-based perspective. Additionally, the project will benchmark existing design processes within its sociotechnical context; and articulate how and why digital transformation could improve existing practices.   

Sponsor(s): National Science Foundation, The Engineering Design and Systems Engineering (EDSE) Program under Grant No: CMMI 2129574)

Principal Investigators: PI: Panhal, J. (Purdue) & Szajnfarber Z., Co‑PI: Topcu T.G.

Description: The objective of this proposal is to advance the scientific understanding of Solver-Aware System Architecting (SASA), leading to theory-grounded guidelines and analytic tools that can be used by practitioners. SASA is a new paradigm of system architecting that seamlessly integrates talent and expertise from outside the organizational boundaries into the systems design process right from the initial stages. The central hypothesis is that the joint consideration of architecture, solvers, and contracts, can significantly improve systems design outcomes. From a methodology standpoint, the hypothesis is that it is feasible to develop theory-grounded guidelines for SASA through computational modeling, in conjunction with machine learning. The research plan consists of developing a computational modeling framework for SASA, and using it in a multi-agent reinforcement learning environment to extract guidelines for architecting and design of contracts. Empirical validation of the framework will be carried out using a rich dataset from NASA autonomous robotic arm. Dissemination of the outcomes to the practitioners and the broader community is an important part of the plan, that will be achieved through a set of educational games. 

• Xames, M.D. and Topcu, T.G. (2024), A Rapid Review of How Model-based Systems Engineering is Used in Healthcare Systems. INCOSE International Symposium, 34: 1447-1462. https://doi.org/10.1002/iis2.13218

• Dharmarajan, A.C., Topcu, T.G., Szajnfarber, Z., and Panchal J., (2024) ”Valuing Outliers: A Modeling Framework to Consider Non‑Traditional Solutions From Non‑Traditional Solvers” ASME IDETC/CIE2024, Washington, DC, USA.
• Shefa, J. and Topcu, T.G. (2024) ”Towards Transparent Operations and Sustainment: A Conceptual Framework for Causal Interpretable Machine Learning Models for System Health Prognostics and Maintenance” Conference on Systems Engineering Research (CSER), Tucson, AZ, USA, 
• Hussain, M., Wach, P., and Topcu, T.G. (2024) ”Can Large Language Models Accelerate Digital Transformation by Generating Expert‑Like Systems Engineering Artifacts?” Conference on Systems Engineering Research (CSER), Tucson, AZ, USA,
• Xames, M.D. and Topcu, T.G. (2023) ”Toward Digital Twins for Human in the loop Systems: A Framework for Workload Management and Burnout Prevention in Healthcare Systems,” IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI), Orlando, FL, USA, 2023, pp. 1 6. doi: 10.1109/DTPI59677.2023.10365449.
• Gadi, V.S., Topcu, T.G., Szajnfarber, Z., and Panchal J., (2023) “Heuristics for Solver Aware Systems Architecting (SASA): A Reinforcement Learning Approach” Proceedings of the ASME 2023 IDETC/CIE Volume 3B: 49th Design Automation Conference (DAC) Boston, Massachusetts, USA. August 20–23, 2023. V03BT03A001. https://doi.org/10.1115/DETC2023-115030
• Mohsenirad,S, Triantis, K., and Topcu, T.G., (2023) “Multi effect Evaluation of Policy Intervention in System Dynamics: A Data Envelopment Analysis Approach” IISE Annual Conference and Expo 2023.
• Topcu, T.G., Triantis, K., and Roets B. (2022). ”Identification of Adverse Operational Conditions in Sociotechnical Systems: A Data Analytics Approach.” In: Recent Trends and Advances in Model Based Systems Engineering, Springer, Cham. https://doi.org/10.1007/978-3-030-82083-1_12
• Mukherjee, S., Hennig, A., Topcu, T. G., & Szajnfarber, Z. (2021). When Decomposition Increases Complexity: How Decomposing Introduces New Information Into the Problem Space. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 85420, p. V006T06A050). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2021-71917
• Hennig, A., Topcu, T. G., & Szajnfarber, Z. (2021). Complexity should not be in the eye of the beholder: how representative complexity measures respond to the commonly-held beliefs of the literature. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 85420, p. V006T06A047). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2021-69598
• Madsen, P. M., Dillon-Merrill, R., Triantis, K., Roets, B., & Topcu, T. (2021). Organizational moderators of the effect of autonomous technology on organizational error prevention. In Academy of Management Proceedings (Vol. 2021, No. 1, p. 13662). Briarcliff Manor, NY 10510: Academy of Management. https://doi.org/10.5465/AMBPP.2021.13662abstract
• Dillon, R., Madsen, P., Roets, B., Topcu, T., & Triantis, K. (2020). The Autonomous Decision System Choice. In 20th Annual Workshop on the Economics of Information Security (WEIS), Brussels.
• Topcu, T. G., & Mesmer, B. (2015). Customer, commercial, and government value functions for electric vehicle system design. In IIE annual conference. proceedings (p. 959). Institute of Industrial and Systems Engineers (IISE).