Sumarry of work performed in the projcet and results achieved with respoect to its objectives:
- We have developed two sets of ML methodologies dimensionality reduction and surrogate modelling, including, namely DMAP and Convolutional Autoencoders (CAEs) (Deliverable D 2.1 and ref [1-3].)
- We are continuously developing and improving AI-Solve library fusing data-driven methods and surrogate models with efficient block-iterative sparse linear system solvers. The proof of concept for this library has been published (ref [4]).
- We are continuously optimising MSolve to fully utilise the combined CPU and GPU potential of modern supercomputers. We demonstrated software capabilities by providing performance and correctness test on physics-based computational models.
- We are extending and upgrading the Korali engine to include state of the art sampling algorithms that harness extreme computation architectures (Deliverable D4.1).
- We have developed 3D image and data processing routines that extract geometries together with estimates of their uncertainties that can be propagated to predictive simulators (Deliverables D5.1 and 5.2).
- The combined Korali and MSolve/AI-Solve machinery has been successfully integrated and tested in Piz Daint HPC platform (Deliverables D6.1)
- We are in the process of producing the baseline frameworks DCOMEX-BIO and DCOMEX-MAT for the applications of WP7
- Continuous monitoring-evaluation of the software in terms of scalability, parallel efficiency, energy efficiency, and data locality is in progress. Energy efficiency considerations are foreseen at a later stage of the project progress.
- We have applied DCoMEX framework multiscale material design applications and presented the results in corresponding Journal Papers and Conferences
- We have presented in DCoMEX platform as novel approach applicable to engineering problems to corresponding exhibitions
