Sergey Kravchenko

Assistant Professor

Ph.D. (Purdue)

Research Interests

Polymers and polymer composites; Multi-functional composites; High-throughput manufacturing process modelling of composites; Multi-scale, multi-physics, probabilistic computational modelling of composites; Manufacturing-informed performance simulation of composites; Machine learning methods in composites manufacturing and performance analysis; Composite structures for renewable energy & unmanned vehicles; Additive manufacturing.

Current Research Work

My research interests are at the interface of materials engineering, structural mechanics, and manufacturing with research focus being the application of high-performance, polymer-based composites for a sustainable mobility. Innovations in composite material products and processes can become instrumental for cost-efficient, environmentally justified solutions for lightweight/high-performance engineering structures. An important driver that can push forward the composite technology’s wider adoption and accelerated insertion is a deeper understanding of complex phenomena involved in the manufacturing and operation stages and a comfortable, casual use of such knowledge. Computational tools, a.k.a. simulations, that account for probabilistic, multi-scale, and multi-physics aspects of a composite technology can greatly aid in developing the necessary fundamental knowledge and ensure its further transformation into the science-advised, applied solutions to industry-relevant problems. I am working on combining together digital twins of manufacturing processes and end-product performance of polymers and their composites, along with training approximate deep neural surrogates of existing stochastic simulators, to explore conditions, materials, geometries, and processes to guide the manufacturing system advances.


Please see my Google Scholar page for the most up-to-date list of publications.