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has opened new perspectives. Neural networks, particularly deep architectures, have demonstrated remarkable capabilities in learning complex nonlinear mappings. Physics-informed neural networks (PINNs
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. Fine-tuning these filtering parameters can greatly improve the stability and robustness of location accuracy, and deep learning techniques show promise in automating and optimizing this process, making
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strong expertise in: • Machine learning, particularly transformer-based models and deep learning frameworks (e.g., PyTorch, TensorFlow). • Multimodal data processing and graph-based modeling
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, Mechanics of Materials, Elasto-Plasticity Topic open: Model-free data-driven approaches offer an interesting alternative to classical constitutive model-based approaches, and to machine-learning approaches
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characterization: Using X-ray tomography, imaging analysis with classical or deep learning tools will be conducted to determine the binder's spatial distribution inside the pores. 1.2. Numerical Modeling