From {\tt Ferminet} to PINN. Connections between neural network-based algorithms for high-dimensional Schrödinger Hamiltonian

• URL: http://arxiv.org/abs/2410.09177v2
• Date: Wed, 20 Nov 2024 16:54:12 GMT
• Title: From {\tt Ferminet} to PINN. Connections between neural network-based algorithms for high-dimensional Schrödinger Hamiltonian
• Authors: Mashhood Khan, Emmanuel Lorin,
• Abstract summary: In particular, we re-formulate a PINN algorithm as a it fitting problem with data corresponding to the solution to a standard Monte Carlo algorithm. Connections at the level of the optimization algorithms are also established.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this note, we establish some connections between standard (data-driven) neural network-based solvers for PDE and eigenvalue problems developed on one side in the applied mathematics and engineering communities (e.g. Deep-Ritz and Physics Informed Neural Networks (PINN)), and on the other side in quantum chemistry (e.g. Variational Monte Carlo algorithms, {\tt Ferminet} or {\tt Paulinet} following the pioneer work of {\it Carleo et. al}. In particular, we re-formulate a PINN algorithm as a {\it fitting} problem with data corresponding to the solution to a standard Diffusion Monte Carlo algorithm initialized thanks to neural network-based Variational Monte Carlo. Connections at the level of the optimization algorithms are also established.

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