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ReachOptimum@TEMA

ReachOptimum TEMA-UA logo

ReachOptimum@TEMA is a multidisciplinary research group at the Centre for Mechanical Technology and Automation (TEMA), University of Aveiro. We explore optimisation, data-driven decision support, and intelligent systems for engineering challenges with a strong focus on water systems and computational mechanics.

🚀 What we do

  • 🎯 Optimization & Operations Research – metaheuristics, multi-objective optimisation, robust and sustainable decision-making

  • 🤖 AI/ML for Engineering – predictive modeling, explainable AI, digital twins, and data-driven simulation

  • 💧 Water Systems – smart water networks, leak detection, operational optimisation, and resilience

  • ⚙️ Computational Mechanics - simulation, optimisation, constitutive modelling, parameter identification, and full-field experimental data

  • 🔓 Open Science & Reproducibility – open datasets, notebooks, reference implementations, and reusable computational tools

💧 Water systems projects

Develop smart, efficient, and resilient water systems, combining optimization, artificial intelligence, and real-time decision support for water distribution networks. Our research supports integrated leak detection, operational planning, energy-aware control, and improved service quality in water utilities.

CENTRO2030-FEDER-01177300 • I-ReTiS-LeaksD&Op

🌐 Website🗂️ Results & dissemination

2024.07270.IACDC • I-ReTiS-Leaks

🌐 Website🗂️ Results & dissemination

COMPETE2030-FEDER-00823400 • I-ReTis-LeaksD&Op

🌐 Website🗂️ Results & dissemination

⚙️ Computational mechanics projects

Develop data-efficient, interpretable computational mechanics models that combine finite element simulation, inverse identification, and machine learning to improve the reliability of advanced sheet metal forming predictions.

COMPETE2030-FEDER-00778700 • LSD-TRIP

🌐 Website

📫 How to reach us

🌐 Website🔗 LinkedIn🔴 YouTube✉️ Email

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  1. AntonioAndrade-Campos_Benchmark2018 AntonioAndrade-Campos_Benchmark2018 Public

    Forked from gilacampos/Benchmark2018

    Different versions of the well known benchmark

    Python

  2. bouncing-ball bouncing-ball Public

    Classic bouncing ball engineering simulation challange.

    Python

  3. mlpcp-interp-num mlpcp-interp-num Public

    Study of interpolation applied to datasets on ML for predicting constitutive parameters.

    Fortran 1

  4. mlpcp-interp-dic mlpcp-interp-dic Public

    Study of interpolation applied to DIC datasets on ML for predicting constitutive parameters.

    Fortran

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