About SimEA
| The project Modeling and Simulation for Engineering Applications (SimEA) was carved to respond to the challenges and opportunities arising when advanced computing and data science are utilized to solve engineering problems. It is funded by the EU and coordinated by CaSToRC in CyI. The SimEA ERA Chair will expand the research portfolio of CaSToRC to include Computation-Based Engineering, collaborate with other research groups in Cyprus, Eastern Mediterranean and internationally, enrich the educational programs of CaSToRC and CyI and set the appropriate mechanisms to forge collaboration with Industry Partners. The project is developing a research team of outstanding researchers, led by Professor V. Harmandaris, appointed as the SimEA ERA Chair. |  |
The research team will work on the development of mathematical and computational methodologies for complex molecular systems, with important applications in nano/bio technology. The ultimate goal is the “computer design of materials and processes” via novel algorithmic approaches and computational tools; i.e. computer engineering of complex materials. The team will pursue a program of research excellence and innovation, by applying and developing mathematical and computational methods including multiscale modeling, physics-based and/or data-driven molecular models, uncertainty quantification, and machine learning methods, integrated with High-Performance Computing, for tackling challenging problems in different application areas related to Computational Science and Engineering. Examples include a broad range of systems/materials of great scientific and technological interest, such as nanocomposites, polymers, graphene-based nanostructured materials, proteins, and biomolecular systems.
SimEA responds to these challenges and opportunities, by encompassing information technology and mathematics with material science and other scientific disciplines to advance discovery and innovation through computation. SimEA’s objectives will be attained via research on:
- Multi-scale Modelling and Simulations of Nanostructured Materials
- Data-driven Machine Learning Approaches for Modelling Across Scales
- Modelling of Biomolecular Systems for Biotechnology Applications
- Bayesian Inference for Uncertainty Quantification and Model Selection
