Jitesh Panchal

Professor and Associate Head of Undergraduate Programs, Purdue University
“Developing Multi-Fidelity Test Plans for Evolving and Heterogeneous AI-Enabled Systems”

Speaker Bio: 

Dr. Jitesh Panchal is a Professor and Associate Head of Undergraduate Programs in the School of Mechanical Engineering at Purdue University. He received his BTech (2000) from the Indian Institute of Technology (IIT) Guwahati and MS (2003) and PhD (2005) in Mechanical Engineering from Georgia Tech. His research interests are in (1) design at the interface of social and physical phenomena, (2) computational methods and tools for digital engineering, and (3) secure design and manufacturing. He is a recipient of the NSF CAREER award and the Distinguished Alumni Award from IIT Guwahati. He received the Young Engineer Award, Guest Associate Editor Award, and three best paper awards from the ASME CIE division. He was recognized by the B.F.S. Schaefer Outstanding Young Faculty Scholar Award, the Ruth and Joel Spira Award, and as one of the Most Impactful Faculty Inventors at Purdue University. Dr. Panchal has co-authored two books and co-edited one book on engineering systems design. He served as an NSF Expert for the Engineering Design and Systems Engineering (EDSE) program and as an Associate Editor for the ASME Journal of Mechanical Design (JMD) and the ASME Journal of Computing and Information Science in Engineering (JCISE).

Abstract: 

The test and evaluation (T&E) of AI-enabled and autonomous systems faces growing challenges due to high costs, long timelines, and the increasing use of heterogeneous AI components that evolve over time. These challenges are particularly acute in Department of Defense acquisition contexts, where AI/ML components are often developed by third-party vendors, limiting access to training data, developmental test results, and assumptions about operational environments. As a result, existing AI T&E approaches do not adequately support operational decision-making under realistic constraints.

This talk presents a risk-informed, multi-fidelity approach to developing efficient test plans for AI-enabled systems that leverages information generated throughout the systems engineering lifecycle. By strategically combining results from lower-fidelity and developmental test environments with targeted high-fidelity operational testing, the proposed approach enables decision-makers to reduce uncertainty about requirement satisfaction while controlling cost and schedule. Sequential test planning methods are used to prioritize tests based on their expected value in reducing risk relative to mission and performance requirements. The approach is demonstrated using an autonomous vehicle (AV) perception system example, showing how informed combinations of tests across multiple fidelity levels can achieve confidence in system performance at substantially lower cost than relying solely on high-fidelity testing. The results illustrate how multi-fidelity, information-driven T&E can support integrated testing, continuous evaluation, and more timely acquisition decisions for AI-enabled autonomous systems.