This paper introduces Mollifier Layers, a novel, lightweight module designed to enhance Physics-Informed Machine Learning (PhiML) by replacing recursive automatic differentiation with convolutional operations. While traditional methods like Physics-Informed Neural Networks (PINNs) struggle with computational costs, memory blow-up, and noise instability when calculating high-order derivatives, this new approach uses analytically defined smooth kernels to transform differentiation into stable integration. By decoupling derivative evaluation from network depth, the architecture achieves significant improvements in memory efficiency and training speed while remaining agnostic to the underlying model. The authors rigorously benchmark the tool across various systems, including Langevin dynamics, heat diffusion, and complex fourth-order reaction-diffusion equations. To demonstrate real-world utility, the method is applied to super-resolution chromatin imaging, successfully inferring critical biophysical reaction rates from noisy biological data. Ultimately, Mollifier Layers provide a scalable and robust framework for solving inverse problems in scientific and biomedical research.

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