Mo-SML-04 - Jacobian-Initialized Parametric Physics-Informed Neural Network for Anomaly Position and Size Estimation from Simulated Electrical Impedance Tomography Data

Event

EUROSENSORS 2026
2026-09-06 - 2026-09-09
Zurich

Band

Poster

Chapter

Signal Analysis, Machine Learning, Physics-Informed Machine Learning And Artificial Intelligence For Sensing

Author(s)

S. Sharma, K. Eckert, Z. Lei - Helmholtz-Zentrum Dresden-Rossendorf,Dresden (Germany)

Pages

694 - 695

DOI

10.5162/eurosensors2026/Mo-SML-04

ISBN

978-3-910600-12-6

Price

free

Abstract

Healthcare is undergoing a paradigm shift from reactive treatment to preventive, personalized, and globally accessible care. Realizing this transformation requires fundamentally new bioanalytical and diagnostic technologies that can deliver rapid, accurate, and affordable results. Towards this goal, our laboratory is pioneering next-generation nanophotonic platforms that enable ultra-sensitive, quantitative, multiplexed, real-time detection of biomolecules through advances in optical biosensing, imaging, and spectroscopy. Nanophotonics provides a powerful technological foundation for bioanalytical devices by enabling strong light–matter interactions with confined lights at subwavelength scales. Building on this capability, we engineer nanophotonic metasurfaces operating over a broad spectrum from visible to mid-infrared to leverage complementary optical detection principles such as label-free refractometric sensing, molecular, structural and chiral sensitive surface enhanced spectroscopy, ultra-sensitive digital nanophotonic biosensing and multi-modal chemical-hyperspectral imaging approaches.