FAU MoD Lecture: Hybrid Modeling and System Identification: Past and Future Directions

Date: Mon. February 16, 2026
Event: FAU MoD Lecture
Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)

FAU MoD Lecture: Hybrid Modeling and System Identification: Past and Future Directions
Speaker: Prof. Dr. Helon Hultmann Ayala
Affiliation: PUCPR, Pontifical Catholic University of Paraná (Brazil)

Abstract. This talk surveys our recent work in hybrid system identification, where physical structure is combined with black-box components to improve predictive performance. We showcase experimental case studies spanning flexible-joint manipulators, drill strings, and rotating elements subject to nonlinear friction. Across these distinct setups, we show that the most effective hybrid architecture varies substantially, underscoring the need for systematic experimentation and problem-specific model evaluation. We conclude by introducing a new dataset for hybrid identification that fuses vision with proprioceptive sensing, presenting preliminary results and discussing its implications for future work in multimodal model learning.

OUR SPEAKER

Helon Vicente Hultmann Ayala is a Professor and Research Scientist with 15+ years of experience spanning academia and industry. He is an Associate Professor (Professor Adjunto) at the Pontifical Catholic University of Paraná (PUCPR), Brazil, and a Research Productivity Fellow of Brazil’s National Council for Scientific and Technological Development (CNPq). His applied research background includes positions at IBM Research (Natural Resources Analytics), Embraer (simulation and control of pneumatic/hydraulic systems), and ZF Friedrichshafen in Germany (modeling and control of limited-slip differentials), and he previously served as a Professor at PUC-Rio. He is currently a visiting researcher at FAU DCN-AvH at Friedrich-Alexander-Universität Erlangen-Nürnberg under an ERC/CNPq agreement, focusing on hybrid system identification, machine learning, and model predictive control.

AUDIENCE

This is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.

WHEN

Mon. February 16, 2026 at 11:30H (Berlin time)

WHERE

On-site / Online

This event @LinkedIn

 
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