• Physical principles: mechanical, acoustics, electrical, thermal.
• Fatigue and fracture.
• Signal processing: advanced signal and/or processing, statistical data analysis, data mining/fusion, machine learning, inverse methods, big data.
• Modelling and simulation applied to experimental mechanics: finite element modeling, boundary element methods, hybrid numerical experimental techniques, etc.
• Sensors and sensor networks: novel smart sensors for experimental mechanics applications, electric and piezoelectric/magneto-electric sensors, nano/micro sensors, wireless sensors.
• Optical techniques: digital image correlation, fiber optics, laser-vibrometer applications, speckle interferometry.
• Thermal Methods and infrared imaging: experimental thermos-mechanics, thermoelastic stress analysis, thermal NDT, data fusion.
• Inverse Methods: material modelling and characterization/identification, machine learning for big data analyses, virtual field method.
• Experimental mechanics applied to bio- and nanotechnologies, single cells, tissues, and biosystems.
• Experimental mechanics applied to nondestructive evaluation and/or structural health monitoring.
• Full-scale structural demonstrations and applications.
• Education: effective teaching for the classroom and the lab.
• Computing: AI and machine learning in experimental mechanics.