Audine Subias
Papers
2
Total Citations
35
H-Index
2
About
Audine Subias is a leading researcher in the diagnosis and analysis of complex dynamic systems, with a primary focus on discrete-event systems and hybrid systems. Her work bridges theoretical model-checking with practical identification techniques, making significant contributions to fault diagnosis and system identification. Subias’s most cited paper, "Diagnosability of Event Patterns in Safe Labeled Time Petri Nets: A Model-Checking Approach" (2021, 33 citations), extends traditional single-fault diagnosability to more intricate event patterns, enabling earlier and more reliable fault detection in timed systems—a critical advancement for safety-critical applications. More recently, her 2024 paper "Dynamics-Based Identification of Hybrid Systems using Symbolic Regression" (2 citations) pioneers the use of symbolic regression to identify hybrid systems that combine continuous and discrete behaviors, with applications spanning robotics, biological systems, and control. This work showcases her ability to tackle emerging challenges in system identification. Subias’s research has a tangible impact on improving reliability and automation in engineering domains, and her innovative approaches continue to influence both theoretical developments and practical implementations in the field.
Research Focus
Key Achievements
Top Papers
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