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arXiv Machine Learning By Rodion Podorozhny, Nikoleta Theodoropoulou, Jelena Te\v{s}i\'c

Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization

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Physics-informed neural networks (PINNs) are applied to model ion‑electronic drift‑diffusion in Pt/SrTiO₃/Si memristive heterostructures, overcoming numerical stiffness and multiscale spatial challenges. A cascaded PINN architecture with a custom Chebyshev spectral optimizer (DSO V2 Hybrid) isolates potential, carrier density, and vacancy transport into four sequential sub‑networks, avoiding condition numbers above 10¹⁶. The surrogate reproduces experimental conductive‑AFM current‑voltage hysteresis with R² > 0.96, maintains Poisson consistency, and offers differentiable inverse parameter estimation and linear‑time inference compared to conventional finite‑element solvers.

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