Collaborative and Biomech

Motivation of the study. Many real-world phenomena produce time series representing the evolution of physical states, examples include the measurement of neuronal activity in neuroscience, the tracking of mechanical systems in engineering, and the monitoring of ecological processes. However, these data are often noisy, incomplete, or measured at different sample rates, making it challenging to reconstruct the true underlying signals. Standard smoothing can help, but neglecting known physics may lead to inaccurate results, especially with poor or sparse data. In contrast, physics-informed methods embed mechanistic insights into the smoothing framework, leveraging prior knowledge to guide state reconstruction. Beyond state trajectories reconstruction, model identification –… Read more