Civil Engineering Professor Ioannis Kougioumtzoglou Wins NSF Career Award
Ioannis Kougioumtzoglou, assistant professor of civil engineering and engineering mechanics, is the fifth junior faculty member at Columbia Engineering this year to win a Faculty Early Career Development (CAREER) award from the National Science Foundation (NSF). He joins Daniel Esposito, Agostino Capponi, Karen Kasza, and James Teherani in receiving the NSF’s most prestigious award in support of the early career-development activities of junior faculty.
Ioannis Kougioumtzoglou
The five-year grant will fund Kougioumtzoglou’s research on analyzing complex engineering systems that have random behavior. Specifically, his project, “A Path Integral Methodology for Accurate and Computationally Efficient Stochastic Analysis of Diverse Dynamical Systems,” is focused on fundamental research to adapt, extend, and apply path-integral-based mathematical tools and techniques from theoretical physics to determine the response and assess the reliability of stochastic engineering dynamical systems.
The analysis and eventual design of these structural and mechanical systems, whose applications range from nano-resonators to civil infrastructure, require powerful mathematical tools that can account not only for complex response behaviors but also for the presence of uncertainties in the modeling process. Current state-of-the-art analysis techniques are either highly accurate or computationally efficient, but not both.
“It has been very difficult to develop a methodology for proper system analysis, design, and optimization that is both fast and accurate,” explains Kougioumtzoglou, who joined Columbia Engineering in 2014. “But we think we are on the right track. Our preliminary results are already very promising. With this NSF support, we are hoping to create a paradigm shift in the way modern engineering systems and structures are analyzed and eventually designed under the presence of uncertainties.”
Kougioumtzoglou’s research interests focus on the general area of mathematical modeling and dynamics of complex structural/mechanical systems with emphasis on uncertainty quantification aspects. The methodology proposed in his NSF project is unique in that it provides both considerable accuracy and computational efficiency and promises, he says, “to push the current capabilities of stochastic analysis to unprecedented levels.”
In order to achieve this high accuracy, including even estimating the probability of highly rare events, Kougioumtzoglou’s approach is to account for higher order terms (fluctuations) in related path integral expansions. At the same time, he will quantify the error of the methodology. To achieve high computational efficiency and account for a large number of stochastic dimensions (>100), he will explore highly sparse representations for the system response in conjunction with appropriate optimization algorithms. His methodology aims to be versatile, accounting for cases of complex stochastic excitation and system modeling including fractional derivatives, and non-Gaussian, nonlinear, and hysteretic response behaviors.
If successful, Kougioumtzoglou’s technique could have a major impact on a number of emerging technologies, including nano-mechanics and energy harvesting. This could lead to a revolutionary change in the optimization and design of diverse engineering systems, such as nano-mechanical oscillators, vibratory energy harvesters, and civil infrastructure systems, with an eventual reduction in cost and an increase in the quality of such systems and structures.