This project focuses on optimizing the selection of materials for manufacturing processes by simulating their behavior under different conditions and providing data-driven insights to aid in decision making. By utilizing advanced algorithms and data analysis techniques, the project aims to reduce costs, improve efficiency, and promote sustainability in manufacturing industries.

The metamaps simulation tool is a project that allows users to model and simulate complex systems, providing insights into network dynamics and visualizing system behavior. It is designed for researchers, analysts, and decision-makers seeking to understand and optimize the performance of complex systems.

Bebida-on-Batsim is a project that implements the Bebida job scheduling strategy over the Batsim simulation platform, allowing for the optimization of HPC and big data job scheduling with preemption. It provides a framework for simulating and analyzing different job scheduling strategies, as well as managing job dependencies and resource requirements.

This project focuses on providing a platform for testing and simulating geothermal systems, analyzing and visualizing the data generated from these tests. It aims to help researchers and engineers in the field of geothermal energy to better understand and optimize the performance of geothermal systems.

The `ask` project is an open-source tool that provides support for scientific workflows, with a focus on simulation and optimization. It is built using OpenMOLE, a scientific workflow platform that enables the execution of complex simulations and optimization algorithms on various computing resources.

The starpu_example_stencil project is a computational framework designed to solve partial differential equations and perform numerical simulations. It leverages the StarPU scheduling and task management library to optimize scientific computations, enabling faster and more efficient simulations.

Starpu_example_dgemm is a project that provides an example of how to use the StarPU scheduling and task programming library to optimize and parallelize matrix multiplication operations. It is designed to accelerate numerical computations and scientific simulations by taking advantage of multi-core and many-core architectures.

The oneAPI-intel project is a unified programming model that allows developers to build high-performance applications for various architectures, including CPUs, GPUs, and FPGAs. It provides a set of libraries, tools, and extensions for optimizing computing-intensive workloads, such as AI, data analytics, and scientific simulations.

The oneapi-nvidia project focuses on optimizing deep learning models for NVIDIA GPUs, developing high-performance computing applications, and accelerating scientific simulations using NVIDIA hardware. It provides tools and libraries for developers to leverage the power of NVIDIA GPUs and improve the performance of their data-intensive workloads.

The cmake_external_scotch project is a collection of tools for managing complex data structures, optimizing numerical simulations, and partitioning large graphs. It is built using the SCOTCH and PT-SCOTCH libraries, which provide efficient algorithms for graph partitioning, sparse matrix ordering, and related tasks.