A New Iterative Framework for Efficient and Stable Partial Differential Equation Solutions
A novel iterative framework, driven by Partial Differential Equation (PDE) energy, promises more efficient and stable solutions. This innovative approach bypasses traditional matrix-based discretizations and costly training of learning models, evolving random initial fields through physically constrained diffusion iterations. Results show stable convergence and accuracy, offering a flexible and scalable alternative for research and engineering applications, with positive implications for TCO in on-premise contexts.