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Automatic Differentiation for Inverse Problems with Applications in Quantum Transport

arXiv 2307.09311 ↗First posted 18 Jul 2023cs.LG cs.CE physics.comp-phcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 18 Jul 2023.

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References · 4

  1. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  2. 2020Universal Differential Equations for Scientific Machine Learningcited by 353
  3. 2020AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradientscited by 283
  4. 2023Predictive Limitations of Physics-Informed Neural Networks in Vortex Sheddingcited by 12

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