Characterization of Thermal Systems from Noisy and Low-resolution Measurements Using Dynamic Mode Decomposition

This chapter explores the application of Dynamic Mode Decomposition (DMD) for characterizing thermal systems using noisy and low-resolution measurement data. It focuses on preprocessing and truncation strategies to improve stability and interpretability, considering cases like forced convection and transient heat conduction. The study demonstrates that DMD can effectively recover dominant thermal behavior from sparse and degraded datasets with appropriate truncation, balancing reconstruction fidelity and noise sensitivity.