CoRoPINN: Cognitive region optimized physics-informed neural networks

The solution of partial differential equations (PDEs) remains a significant problem in scientific computing. Although physics-informed neural networks (PINNs) provide a mesh-free paradigm, pointwise constraints offer limited supervision between collocation points and uniform training can under-resolve regions with greater learning difficulty. To address these coupled limitations, this paper introduces Co gnitive R egion O ptimized P hysics- I nformed N eural N etworks (CoRoPINN). Rather than treating region optimization and cognitive learning as independent additions, CoRoPINN forms a closed feedback loop. Gradient-stability statistics calibrate a globally stable baseline radius, PDE residuals estimate pointwise learning difficulty, and the same difficulty signal jointly adjusts sample-specific neighborhoods and cognitive weights. This coupling expands continuous physical constraints around under-resolved collocation points while progressively reallocating optimization from easier to more difficult regions. We evaluate CoRoPINN on three one-dimensional PDEs with five backbone architectures, Poisson equations in two, three, and five dimensions, and Burgers2D. Across the five backbones, CoRoPINN achieves the lowest geometric-mean relative mean absolute error (rMAE) on all three one-dimensional benchmarks, reducing the aggregate error relative to the second-best method by 21.0%, 61.6%, and 52.9% on 1D-Reaction, 1D-Wave, and Convection, respectively. It also ranks first in both metrics for 2D and 5D Poisson, remains second in 3D, and attains the lowest rMAE and relative root mean square error (rMSE) among all four methods on Burgers2D. These results establish a clear overall accuracy advantage for CoRoPINN among the evaluated baselines.