Structural identification of material properties and load parameters using a differentiable material point method

This study introduces a novel optimization framework based on the differentiable material point method (DiffMPM) for the identification of structural and loading parameters from dynamic structural monitoring data. Structural health monitoring (SHM) methodologies are often challenged by the need to interpret limited and noisy measurements for unknown parameter identification, particularly in dynamic environments where external forces are not directly observable. DiffMPM addresses this challenge by integrating a particle‐based continuum model with automatic differentiation, enabling the inference of spatially varying elastic properties and loading parameters from dynamic strain data. In contrast to conventional model updating techniques, DiffMPM provides a fully differentiable simulation pipeline that enables efficient gradient‐based parameter estimation. A comprehensive series of numerical experiments validates the framework's robust performance under conditions of varying mesh resolution, measurement point density, initial parameter assumptions, and measurement sensor noise. This work highlights the potential of differentiable physics for SHM applications requiring interpretable estimation of structural and loading parameters.