The AIAA Intelligent Systems Technical Committee (ISTC) is concerned with the application of Intelligent System (IS) technologies and methods to aerospace systems, the verification and validation of these systems, and the education of the AIAA membership in the use of IS technologies in aerospace and other technical disciplines.
IS Technical Seminar Series
The Intelligent Systems Technical Seminar Series has been opened up to the general public for 2022-2023! Recordings are taken and available for viewing after the fact, for those interested.
Keep an eye out on our LinkedIn / Facebook / AIAA Engage pages for announcements of the next seminars, or watch our public Google Calendar!
Srikanth Saripalli – Wednesday, June 14, 2023
Speaker: Professor Srikanth Saripalli
Mechanical Engineering department
Texas A&M University
Date/time: Wednesday, June 14th, 2023 – 5:00pm-6:00pm Eastern time
Title: High Speed Off-Road Autonomy: Perception and Control in the Wild
Meeting recording: To-be-posted
Abstract: The talk focuses on perception and planning algorithms for autonomous vehicles in off-road situations. A particular emphasis is on why off-road vehicles are different than on-road vehicles and how can we solve autonomy in the off-road domain. A major portion of the talk will be on applications of the above algorithms to real vehicles and the lessons that we have learned i.e. what worked and what didn’t and how we should go about building such systems. I will also briefly touch on our work on Autonomous Landing and Obstacle Avoidance for UAVs.
Bio: Srikanth Saripalli is a Professor in Mechanical Engineering department and the Director for Center for Autonomous Vehicles and Sensor Systems (CANVASS) at Texas A&M University. He holds the J. Mike Walker ’66 Professorship. His research focuses on robotic systems: particularly in air and ground vehicles and necessary foundations in perception, planning, control and system integration for this domain. He is currently leading several efforts in off-road autonomous ground vehicles. He has also led several long-term (> 6 month) on-road deployments of autonomous 18 wheeler trucks and slow-moving shuttles in Texas. He is currently interested in developing and deploying Autonomous Shuttles on campus and in cities. He is also interested in developing such autonomous shuttles for mobility challenged and para transit applications.
Flyer: https://drive.google.com/file/d/1xVeI9XYPGJJbaFWfz6zfmXD9gm3c6mpj/view?usp=sharing
Woong-Je Sung – Wednesday, May 24 2023
Speaker: Woong-Je Sung, Ph.D.
Research Engineer, School of Aerospace Engineering
Georgia Institute of Technology
Date/time: Wednesday, May 24th, 2023 – 3:00pm-4:00pm Eastern time
Title: Deep Learning Strategy for Aerodynamics
Meeting recording: To-be-posted
Abstract: The recent progress in deep learning and generative AI provides active challenges as well as remarkable opportunities in aerodynamics research where the highly non-linear flow phenomena (e.g., shock waves and flow separations) are not uncommon and the available data are not always abundant considering the high dimensionality of flow boundary conditions. In this talk, first, the applications of deep learning techniques are briefly reviewed in the context of surrogate modeling and dimensionality reduction and, second, several research snapshots are discussed including a CFD (Computational Fluid Dynamics)-WTT (Wind Tunnel Test) data fusion using deep representation learning, a geometric deep learning for 3-D mesh, and an aerodynamic shape optimization using deep reinforcement learning.
Bio: Dr. Woong-Je Sung Studied CFD, FEM, and MDO in Seoul National Univ (BS/MS in Aerospace Engineering). He worked on experimental and computational aerodynamics in Agency for Defense Development (1999-2004, Korea). He studied meta-modeling with neural network in Georgia Tech (PhD in Aerospace Engineering, 2012). Dr. Sung has worked on various projects on M&S and ML/AI as Post-Doc and Research Engineer in Georgia Tech (2012-Present).
Junfei Xie – Wednesday, March 29, 2023
Speaker: Junfei Xie, Ph.D.
Associate Professor, Department of Electrical and Computer Engineering
San Diego State University
Date/time: Wednesday, March 29th, 2023 – 5:00pm-6:00pm Eastern time
Title: Networked Airborne Computing: Empowering Reliable and Efficient Computing in the Skies
Meeting recording: To-be-posted
Abstract: Unmanned aerial vehicles (UAVs) have emerged as a crucial technology in various civilian and commercial applications. While most UAV applications involve a single UAV, many new applications are expected to demand cooperative computing capabilities among multiple UAVs. This trend presents opportunities for researchers to address fundamental challenges across a range of disciplines, from aerospace to control, communication, networking, and computing. With multiple UAVs sharing computing resources, the networked multi-UAV system can also provide on-demand computing services to ground users, essentially functioning as a flying cloud. However, enabling reliable and efficient networked airborne computing requires overcoming many formidable challenges such as 3-dimensional node mobility, highly uncertain operating environment, and strict safety requirements. In this talk, this new and cross-disciplinary area will be explored, and recent research results we have developed to enable networked airborne computing will be presented.
Bio: Dr. Junfei Xie is an Associate Professor in the Department of Electrical and Computer Engineering at San Diego State University (SDSU). She received her B.S. degree in Electrical Engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2012. She received her M.S. degree in Electrical Engineering in 2013 and Ph.D. degree in Computer Science and Engineering in 2016 from University of North Texas (UNT), Denton, TX. Prior to joining SDSU, she was an Assistant Professor in the Department of Computing Sciences at Texas A&M University-Corpus Christi (TAMUCC). Dr. Xie’s research interests span several areas, including large-scale dynamic system design and control, unmanned aerial systems, networked airborne computing, mobile edge computing, air traffic flow management, uncertainty quantification, spatiotemporal data analysis, and complex information systems. She is the recipient of multiple prestigious awards such as the NSF CAREER Award, SDSU Presidential Research Faculty Fellow Award, Top 50 Women of Influence in Engineering by San Diego Business Journal, etc. She currently serves as the Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics – Systems, Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs, and Guest Editor for Unmanned Systems.
Flyer: https://drive.google.com/file/d/1rXQI5mf5B_iEYujxSooeAn-t7h6f6-tD/view?usp=share_link
Stanley Bak – Wednesday, February 15, 2023
Speaker: Stanley Bak, Ph.D.
Assistant Professor, Department of Computer Science
Stony Brook University
Date/time: Wednesday, February 15th, 2023 – 3:00pm-4:00pm Eastern time
Title: Safe Autonomy through Surrogate Verification
Meeting recording: To-be-posted
Abstract: Using autonomy within safety-critical applications demands strong assurances the system will not misbehave. Rather than direct analysis and verification, which can be hard, we instead explore using surrogate modeling. With surrogate modeling, we can create models that closely approximate system behaviors while being more amenable to formal verification methods that can prove system safety. We explore this strategy in two contexts: one for closed-loop neural network control system verification and one for approximating nonlinear dynamical systems using Koopman Operator approximations.
Bio: Stanley Bak received his PhD from the the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) in 2013 and then worked for several years at the Air Force Research Laboratory (AFRL) in the Verification and Validation (V&V) group of the Aerospace Systems Directorate. In 2020, he received the AFOSR Young Investigator Research Program (YIP) award. He is currently an assistant professor in the Department of Computer Science at Stony Brook University.
Flyer: To-be-posted
Chetan S. Kulkarni – Wednesday, December 13, 2022
Speaker: Chetan S. Kulkarni, Ph.D.
Staff Researcher, Prognostics Center of Excellence and the Diagnostics and Prognostics Group
Intelligent Systems Division, NASA Ames Research Center
Date/time: Wednesday, December 14th, 2022 – 4:00pm-5:00pm Eastern time
Title: Hybrid Approaches for Systems Health Management and Prognostics
Meeting recording: AIAA Zoom recording link
Passcode: MS2b6ME^
Abstract: To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system’s components health solving a calibration problem in the deep learning approach.
Bio: Chetan S. Kulkarni is a staff researcher at the Prognostics Center of Excellence and the Diagnostics and Prognostics Group in the Intelligent Systems Division at NASA Ames Research Center. His current research interests are in Systems Diagnostics, Prognostics and Health Management. Specifically focused in the area of developing physics-based and hybrid modeling approaches for diagnosis and prognosis of complex systems. He completed his MS (‘09), Ph.D. (‘13) from Vanderbilt University, TN where he was a Graduate Research Assistant with the Institute for Software Integrated Systems and Department of Electrical Engineering and Computer Science. He completed his BE (‘02) from the University of Pune, India. Prior to joining Vanderbilt, he was a Research Fellow at the Department of Electrical Engineering, IIT-Bombay, where his research work focused on developing low-cost substation automation system monitoring and control devices and partial discharge of high voltage transformers. Earlier to that he was a member of the technical team of the Power Automation group at Honeywell, India where he was involved in turnkey power automation projects and product development in the area of substation automation. He is KBR Technical Fellow, AIAA Associate Fellow and Associate Editor for IEEE, SAE, IJPHM Journals on topics related to Prognostics and Systems Health Management. He has been Technical Program Committee co-chair at PHME18, PHM20 and PHM21. He co-chairs the Professional Development and Education Outreach subcommittee in the AIAA Intelligent Systems Technical Committee.
Flyer: To-be-posted
K. Merve Dogan – Monday, October 24, 2022
Speaker: K. Merve Dogan, Ph.D.
Department of Aerospace Engineering; Assistant Professor
Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA
Date/time: Monday, October 24th, 2022 – 5:00pm-6:00pm Eastern time
Title: Verifiable Adaptive Architectures for Control of Safety-Critical Systems
Meeting recording: AIAA Zoom recording link
Passcode: 7r..TFrc
Abstract: Model reference adaptive control (MRAC) methods are powerful mathematical tools for safety-critical sole and multiagent systems, where they have the capability to suppress the effect of exogenous disturbances and system uncertainties for achieving a desired level of closed-loop system response. However, the closed-loop system stability with these methods can be challenged for a wide array of applications that involve unmodeled dynamics (e.g., rigid body systems coupled with flexible appendages, airplanes with high aspect ratio wings, and high-speed vehicles with rigid body and flexible dynamics coupling) and actuator dynamics (e.g., cooperation of low and high speed autonomous vehicles). Motivated by this standpoint, this seminar will introduce verifiable MRAC architectures for both sole and multiagent systems, where we will show the stability tradeoffs of these architectures in the presence of unmodeled and actuator dynamics as well as system uncertainties. In order to bridge the gap between theory and practice, several simulation and experimental results will be also presented.
Bio: Dr. K. Merve Dogan is an Assistant Professor of the Department of Aerospace Engineering at the Embry-Riddle Aeronautical University and the director of Foundational Autonomous Systems and Technologies Research Group (FAST - https://www.fastresearchgroup.com) since August 2020. Prior to joining the Embry-Riddle Aeronautical University, she held the Research Assistant position in the Department of Mechanical Engineering at the University of South Florida between 2015 and 2020, where she received her Doctor of Philosophy degree in 2020. Before joining the University of South Florida, she held a Research/Teaching Assistant position in the Department of Electrical and Electronics Engineering at the Izmir Institute of Technology between 2012 and 2015, where she received her Master of Science degree in 2016. Dr. Dogan is a Co-Director of the Forum on Robotics and Control Engineering (FoRCE), and is a member of AIAA and IEEE, including several technical committees.
Flyer: To-be-posted
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