Adaptive Vekua Cascade: A Differentiable Spectral Solver for Physics-Informed Learning

This research explores a novel differentiable solver leveraging spectral analysis for physics-informed machine learning. The focus on the Vekua transform and its adaptive cascade suggests a sophisticated approach to solving complex physical systems within a neural network framework.

• •Develops a differentiable solver, enabling end-to-end learning with physical constraints.
• •Employs the Vekua transform, a tool from complex analysis, to incorporate physics knowledge.
• •Targets the problem of physics-informed representation learning using a spectral-analytic approach.