Shurui Lin is currently a 2nd-yr graduate student in University of Illinois, Urbana-Champaign, working with Prof.Xin Liu, after getting his bachelor degree of physics from University of Science and Technology of China. He now works in LSST-DESC on weak-lensing shear estimation. His research focus is applying machine learning technique for weak-lensing cosmology. Weak gravitational lensing shear estimation for Stage-IV surveys requires both sub-percent calibration accuracy and high statistical precision, yet traditional estimators struggle with realistic galaxy complexity while machine-learning methods often introduce biases.I will a physics-informed approach that combines a fully D₄-equivariant convolutional neural network (D₄CNN) with a score-matching technique for optimal shear estimation. The D₄CNN enforces symmetry under rotations and reflections, eliminating even-order shear biases by construction, while Analytical Calibration (AnaCal) provides precise, gradient-based self-calibration.Together with modern denoising-score-matching framework, our method achieves multiplicative biases consistent with zero at the ∼10⁻⁴ level, well within the requirement of Stage IV surveys like LSST, and reduces shape noise by ∼20% relative to the classical baseline, providing a principled and practical machine-learning pathway toward optimal shear estimation for Stage-IV surveys.