  Physics-informed neural networks (PINNs) integrate fundamental physical
principles with advanced data-driven techniques, driving significant
advancements in scientific computing. However, PINNs face persistent challenges
with stiffness in gradient flow, which limits their predictive capabilities.
This paper presents an improved PINN (I-PINN) to mitigate gradient-related
failures. The core of I-PINN is to combine the respective strengths of neural
networks with an improved architecture and adaptive weights containingupper
bounds. The capability to enhance accuracy by at least one order of magnitude
and accelerate convergence, without introducing extra computational complexity
relative to the baseline model, is achieved by I-PINN. Numerical experiments
with a variety of benchmarks illustrate the improved accuracy and
generalization of I-PINN. The supporting data and code are accessible at
https://github.com/PanChengN/I-PINN.git, enabling broader research engagement.
