A compact and efficient physics-informed architecture for reconstructing and predicting complex physical fields from low accuracy sparse data.

Complex physical fields are traditionally characterized through high-fidelity simulations or dense sensor networks, which provide detailed information but are often computationally or experimentally costly. Deep learning offers a promising approach for reconstructing and forecasting such fields from limited observations. However, many existing AI-based models rely on large labeled datasets and heavily parameterized architectures, making the models both data-intensive and computationally demanding to train. To overcome these limitations, we introduce compact and computationally efficient physics-informed neural networks (PINNs) designed to reduce both architectural complexity and computational overhead. We demonstrate its performance to reconstruct and forecast complex fields with limited or low accuracy multiscale spatiotemporal dynamics using laminar and turbulent flows as representative benchmarks. We first demonstrate accurate super-resolution of lid-driven cavity flows at Reynolds numbers up to