A preprint demonstrates that automatic differentiation plus gradient descent can reconstruct spatial profiles from magnetotransport data and perform reverse modeling of disordered lattices.
Automatic Differentiation for Inverse Problems with Applications in Quantum Transport
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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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Backsolution: A Framework for Solving Inverse Problems via Automatic Differentiation
A preprint demonstrates that automatic differentiation plus gradient descent can reconstruct spatial profiles from magnetotransport data and perform reverse modeling of disordered lattices.