AI for Model-Based Design: Accelerating Product Development with MathWorks

This session is for engineers, researchers, and technical leaders who work with high‑fidelity physics‑based or FEA models and need faster, scalable ways to use them in system‑level simulation, control design, or HIL testing. It is especially relevant for those seeking reduced‑order models to speed up simulations, enable real‑time deployment, build virtual sensors, or integrate AI into engineering workflows. Attendees will learn best practices for selecting and creating AI‑based ROMs in MATLAB, reusing detailed component models efficiently, and applying ROMs to controller design and HIL applications.

Mark Biesiada has a Bachelor of Science in electrical engineering from Ohio University, a Master of Science in electrical and computer engineering from Lawrence Technological University, and a graduate certificate in digital forensics from Boston University. Mark has become an expert in Model Based Design, in particular, code generation workflows. His current areas of focus include exploring embedded AI solutions and related development workflows.

Guest Speaker: Michael Tschanz - Michael is a technology consultant and professional musician. He recently retired from Disney where he led a multidiscipline team which developed mathematical/physics models for transportation, ride and animatronic systems, custom software/network applications, and robotics. The responsibilities for this team included the development of optimization algorithms, servo controllers, and interactive/immersive experiences. Michael’s background includes designs of numerous attractions at various Disney theme parks including Test Track®, Mission: SPACE®, Toy Story Mania!®, and Expedition Everest®. Michael also designed the velocity profiles at worldwide locations of The Twilight Zone Tower of Terror™. Michael also led Disney’s Scientific Data Analytics initiative focused on digital twins and attractions predictive maintenance. Prior to Disney, Michael developed precision guidance algorithms and aerospace sensor simulations for Texas Instruments. Michael earned his BSEE from Ohio Northern University and a MSEE from the University of Texas Arlington. Further studies included a preparatory scholarship at the Cleveland Institute of Music in classical piano performance.