Methods that transform continuous equations into discrete forms for numerical solutions in engineering. Discretization techniques approximate derivatives and integrals on grids or particles, enabling simulations via novel grid-based, grid-free, or unfitted approaches. This allows efficient analysis of complex systems.

Approaches that leverage data to predict engineering outcomes. These include machine learning (ML), reduced order models (ROM), and big data to capture patterns, quantify uncertainty, and create digital twins for efficient system analysis and optimization.

Advanced systems for rapid, accurate engineering simulations. They harness optimized algorithms and parallel computing to solve complex numerical problems, enabling cutting-edge analysis with applications in real-world engineering settings.