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Last modified by Ricardo Julio Rodríguez Fernández on 2026/06/14 15:43
From version 3.2
edited by Ricardo Julio Rodríguez Fernández
on 2026/06/14 08:29
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To version 4.1
edited by Ricardo Julio Rodríguez Fernández
on 2026/06/14 08:34
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1 1  === Communicating Generative Machine Learning Models for Neutron Tomography Simulation ===
2 2  ==== Titor: [[Xabier>>https://igfae.usc.es/igfae/persoa/cid-vidal-xabier/159/||target="_blanl]]" Cid Vidal ====
3 -==== Supervisor: María Pereira Martínez
3 +==== Supervisor: [[María>>https://igfae.usc.es/igfae/persoa/pereira-martinez-maria/565/||target="_blank"]] Pereira Martínez
4 4  ====
5 5  
6 6  Neutron tomography is an innovative technique for the detailed analysis of dense materials, allowing for the detection of internal defects and chemical composition without destroying the sample. Optimizing the main detector of the tomograph requires simulations that demand high computational resources. To address this, generative machine learning models offer a revolutionary approach to speed up simulations by learning to mimic the detector's response under different experimental conditions. In this project, the student will implement and train a generative neural network using Python libraries (like TensorFlow or PyTorch) to simulate the neutron tomograph and validate the model against traditional simulations.