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A Machine Learning Approach to Trapped Many-Fermion Systems
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abstract
We apply a variational Ansatz based on neural networks to the problem of spin-$1/2$ fermions in a harmonic trap interacting through a short distance potential. We showed that standard machine learning techniques lead to a quick convergence to the ground state, especially in weakly coupled cases. Higher couplings can be handled efficiently by increasing the strength of interactions during "training".
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Cited by 1 Pith paper
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Kolmogorov-Arnold Wavefunctions
KAN-based trial wavefunctions reach about 1 percent ground-state energy accuracy for one-dimensional trapped bosons at roughly 10 times lower cost per training step than MLP-based wavefunctions, aided by a transferabl...
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