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Automatic Differentiation for Inverse Problems with Applications in Quantum Transport

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arxiv 2307.09311 v1 pith:ICNAUNX4 submitted 2023-07-18 cs.LG cs.CEphysics.comp-ph

Ivan Williams , Eric Polizzi This is my paper
classification cs.LGcs.CEphysics.comp-ph
keywords quantumdifferentiableengineerinverseneuralsimulationsolvertransport
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A neural solver and differentiable simulation of the quantum transmitting boundary model is presented for the inverse quantum transport problem. The neural solver is used to engineer continuous transmission properties and the differentiable simulation is used to engineer current-voltage characteristics.

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  1. Backsolution: A Framework for Solving Inverse Problems via Automatic Differentiation

    cond-mat.dis-nn 2025-06 conditional novelty 4.0 of 10

    A preprint demonstrates that automatic differentiation plus gradient descent can reconstruct spatial profiles from magnetotransport data and perform reverse modeling of disordered lattices.

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