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This deliverable presents the final software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definitions [13]. This release also contains the latest developements on MPI parallel remeshing in ParMmg. This report is meant to serve as a supplement to the public release of the software. Kratos is “a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. XMC is “a Python library for parallel, adaptive, hierarchical Monte Carlo algorithms, aiming at reliability, modularity, extensibility and high performance“. Hyperloom and PyCOMPSs are environments for enabling parallel and distributed computation. ParMmg is an open source software which offers the parallel mesh adaptation of three dimensional volume meshes.

This deliverable presents the software release of Kratos Multiphysics, together with the XMC library, Hyperloom and PyCOMPSs API definition [8]. This report is meant to serve as a supplement to the public release of the software. Kratos is “a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface”. XMC is a python library for hierarchical Monte Carlo algorithms. Hyperloom and PyCOMPSs are environments for enabling parallel and distributed computation.

No abstract

We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from finite element methods and custom loss functions from neural networks are merged to form the algorithm. The Finite Element Method-enhanced Neural Network hybrid model (FEM-NN hybrid) is data-efficient and physics-conforming. The proposed methodology can be used for surrogate models in real-time simulation, uncertainty quantification, and optimization in the case of forward problems. It can be used to update models for inverse problems. The method is demonstrated with examples and the accuracy of the results and performance is compared to the conventional way of network training and the classical finite element method. An application of the forward-solving algorithm is demonstrated for the uncertainty quantification of wind effects on a high-rise buildings. The inverse algorithm is demonstrated in the speed-dependent bearing coefficient identification of fluid bearings. Hybrid methodology of this kind will serve as a paradigm shift in the simulation methods currently used.