There is a growing number of problems where experiments are impossible, hazardous, or extremely expensive.

Extreme-scale computing enables the solution of vastly more accurate predictive models and the analysis of massive quantities of data thanks to AI.
Combining predictive modeling with data, coupled with machine learning and AI strategies, can create new opportunities in science.
In particular, move from Human-in-the-Loop towards hybrid Human and Artificial Intelligence-driven design, discovery, or evaluation.

However, various scientific and technical challenges need to be met to exploit exascale computing capabilities.
These bottlenecks impact methods and algorithms in a profound way on all aspects of the simulation toolchain through :

• (i) avoidance of communication,
• (ii) adaptive parallel grain and more compute-intensive at node level,
• (iii) handling of heterogeneous hardware and data representations and
• (iv) self-parametrization.

The Exa-MA project concentrates on the Exascale aspects of the numerical methods, ensuring their scalability to existing and forthcoming hardware.
Furthermore, it is a transverse project, proposing methods and tools where modeling, data and AI, through algorithms, are central.