Mode-Shape Expansion Using Physics-Constrained Gaussian Process Regression
Researchers have developed a new Physics-Constrained Gaussian Process Regression (CONS-SOGP) framework to improve the reconstruction of structural mode shapes from limited sensor data. This method addresses inconsistencies in standard Gaussian Process Regression by incorporating a mass-orthogonality penalty, ensuring physically plausible results. Numerical tests on a multi-degree-of-freedom structure confirmed that CONS-SOGP provides more accurate and reliable expanded mode shapes compared to existing techniques. AI
IMPACT Introduces a novel statistical method for physical system analysis, potentially improving data-driven modeling in engineering.