Dr. Bouklas' research focuses in the fields of theoretical and computational solid mechanics. Developing theoretical frameworks and advanced computational methods, he aims to improve the fundamental understanding of materials and structures, and enhance the predictive capabilities in relevant engineering applications. He is interested in the fundamental study of soft materials, active materials and biomaterials, fracture and instabilities, as well as multiscale modeling in coupled multi-physical systems. A recent thrust in his lab targets machine learning (ML)-enabled constitutive models and solutions of PDEs using a combination of data and guiding physical principles.