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This paper introduces a model-free deep learning method for solving high-dimensional nonlinear partial differential equations with unknown coefficients, using zeroth-order derivative estimators derived from perturbed Monte Carlo trajectories. The approach avoids automatic differentiation, provides theoretical error bounds, and demonstrates competitive performance in numerical experiments.