Saurabh Puri's blog

Composite materials are increasingly used in industry due to the possibility of tailoring their properties based on the applications. Their greatest advantage is strength and stiffness combined with lightness. However, their optimal design and performance is still limited by the lack of knowledge of physical mechanisms that control their fracture behavior. Machine learning and big data driven approaches have not been extensively studied for fracture behavior predictions.

The focus of this symposium is to discuss current research and key developments in theory, computational and experimental methods to study and predict the mechanical properties of materials in application-orientated environments. These environments may include, but are not limited to high temperature, cryogenic temperature, electrical and magnetic field, gas, radiation, chemical, pressure extremes, and humidity.

The focus of this symposium is to discuss current research and key developments in theory, computational and experimental methods to study and predict the thermo-mechanical properties of materials in application-orientated environments. These environments may include, but are not limited to high temperature, cryogenic temperature, electrical and magnetic field, gas, radiation, chemical, pressure extremes, and humidity.