Our research aims at developing more advanced simulations, with stronger mathematical and biomechanical foundations, and their adaptation to a specific patient. We want, in particular, to develop more stable numerical methods that would, at the same time, be suited for the generation of digital twins of organs. Given that our main field of application is compute-guided surgery, we investigate numerical techniques to achieve for accelerating physic-based simulations to achieve real-time computation; we also study the numerical aspect of contact problems.

Our second research axis is derived from our application context and essentially consists of developing optimization and control methods for computer-assisted interventions. At the core of our activity is the hypothesis that data-driven simulation has the potential to bridge the gap between medical data (most often images) and clinical routine by updating pre-operative knowledge with the information available at the time of the procedure.

Members of the team consider essential to disseminate our research results – and the algorithms to produce them – in an open manner. The objective is to also develop a framework that could be used internally as a mean to integrate our various contributions and facilitate validation and technology transfer. Many of our research results have been released to the community as open source code, either through improvements of SOFA or as plugins of the framework.