  Driven by the need for more efficient and seamless integration of physical
models and data, physics-informed neural networks (PINNs) have seen a surge of
interest in recent years. However, ensuring the reliability of their
convergence and accuracy remains a challenge. In this work, we propose an
efficient, gradient-less weighting scheme for PINNs, that accelerates the
convergence of dynamic or static systems. This simple yet effective attention
mechanism is a function of the evolving cumulative residuals and aims to make
the optimizer aware of problematic regions at no extra computational cost or
adversarial learning. We illustrate that this general method consistently
achieves a relative $L^{2}$ error of the order of $10^{-5}$ using standard
optimizers on typical benchmark cases of the literature. Furthermore, by
investigating the evolution of weights during training, we identify two
distinct learning phases reminiscent of the fitting and diffusion phases
proposed by the information bottleneck (IB) theory. Subsequent gradient
analysis supports this hypothesis by aligning the transition from high to low
signal-to-noise ratio (SNR) with the transition from fitting to diffusion
regimes of the adopted weights. This novel correlation between PINNs and IB
theory could open future possibilities for understanding the underlying
mechanisms behind the training and stability of PINNs and, more broadly, of
neural operators.
