Coarse Mesh

Coarse mesh techniques aim to accelerate computationally expensive simulations, particularly in computational fluid dynamics (CFD) and solid mechanics, by performing initial calculations on a lower-resolution mesh. Current research focuses on using machine learning, particularly deep learning architectures like UNets and graph neural networks (GNNs), to upscale these coarse-mesh results to high-fidelity predictions, often incorporating physics-informed constraints to improve accuracy. This approach offers significant potential for reducing computational costs in various fields, enabling faster design iterations and more efficient scientific discovery in areas like material science and engineering simulations. The development of mesh-independent methods further enhances the applicability and robustness of these techniques.