Magnetic sensing supported by machine learning

This dissertation explores how magnetic sensing can be advanced by integrating machine learning (ML) with magnetically responsive materials. Grounded in the concept of bioinspired adaptation, the core approach leverages soft ferromagnetic assemblies, magnetoelectric interfaces, and mouldable magnetic soft composites as reconfigurable platforms that readily adjust sensing sensitivity and functionality under varying stimuli. Coupled with ML methods—from basic classification to optimization strategies—these magnetically driven sensors transcend conventional static designs, enabling tasks such as shape detection, adaptive mechanosensing, secure information encoding, and magnetic field measurement. The research progresses from trainable, bioinspired sensor concepts to increasingly integrated systems that demonstrate broader functionality and higher autonomy along this spectrum of applications. Underpinning the whole work is the concept of magnetic fields serving not only as a stimulus but also as a tunable “control knob” to reconfigure material properties. Machine learning methods then classify complex patterns, interpret sensor data, and enhance sensing resolution by addressing performance trade-offs. Although the main emphasis is on magnetics, a final demonstration of pressure-based handwriting recognition illustrates the broader applicability of integrating advanced material engineering with ML. The result is a cohesive framework where ML augments magnetic sensing systems toward enhanced adaptability, robustness, and intelligence. By seamlessly uniting magnetic field manipulation with data-driven algorithms, this dissertation proposes a framework for developing advanced sensing devices in applications ranging from soft robotics and biomedical diagnostics to secure communication and wearable electronics. Beyond individual device advancements, the work underscores the broader potential of cross-disciplinary research—where merging materials science, magnetics, and ML can catalyse transformative innovations in sensor design and functionality.