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Machine Learning for Simulating the Deuteron in Nuclear Physics

Utilizing machine learning techniques, a study by Javier Rozalén Sarmiento explores novel architectures for simulating the deuteron in nuclear physics. The research focuses on training artificial neural networks with variational approaches to tackle the deuteron problem, providing insights on uncertainties and error analysis. By leveraging these advancements, the potential for enhancing nuclear physics simulations, particularly in large nuclei systems, is highlighted.

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Machine Learning for Simulating the Deuteron in Nuclear Physics

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