Sparsely Connected Rank-Inspired Neural Network

This paper introduces the Sparsely Connected Rank-Inspired Neural Network for generic PDE numerical solution and function approximation. SCRINN uses a structured sparse connectivity architecture together with an orthogonality- and normalization-guided loss. The method constructs an approximately orthonormal neural basis and promotes progressive rank growth in its evaluation matrix.