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Physics-Informed Neural Networks as Solvers for the Time-Dependent Schr\"odinger Equation
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Physics-Informed Neural Networks as Solvers for the Time-Dependent Schr\"odinger Equation
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We demonstrate the utility of physics-informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schr\"odinger equation. We study the performance and generalisability of PINN solvers on the time evolution of a quantum harmonic oscillator across varying system parameters, domains, and energy states.
Forward citations
Cited by 2 Pith papers
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Operator Learning for Schr\"{o}dinger Equation: Unitarity, Error Bounds, and Time Generalization
A linear estimator for the Schrödinger evolution operator is introduced that enforces weak unitarity, supplies uniform prediction error bounds and time-extrapolation bounds, and reports up to 100x lower relative error...
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Solving Hamiltonian Constraint Equation with Physics-Informed Neural Networks
PINNs with specialized techniques solve the nonlinear Hamiltonian constraint for generic binary black hole initial data, matching traditional NR accuracy.
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