Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning

A recent study highlights the advancements in physics-informed neural networks (PINNs) for solving complex problems in scientific machine learning. This research addresses the common challenge of accurately applying boundary conditions, which has historically limited the effectiveness of PINNs. By introducing normalized distance functions and adaptive weight tuning, the study proposes a more reliable and efficient approach to inverse analysis. This is significant as it enhances the potential of PINNs in various applications, paving the way for improved solutions in fields reliant on partial differential equations.