#graph-neural-networks — 1sec.ai

Acceleration of an algebraic multigrid pressure solver using graph neural networks

Researchers introduced a graph neural network-based smoother for algebraic multigrid pressure solvers, targeting incompressible flow simulations. The approach uses a modified graph convolutional isomorphism network to predict optimal polynomial coefficients, aiming to accelerate solver performance on unstructured meshes. This method addresses the longstanding challenge of mesh irregularities in traditional linear solvers. You can apply this technique to improve the efficiency of flow simulations

Key takeaways

Graph neural network smoother improves algebraic multigrid performance.
Targets incompressible flow simulations with unstructured meshes.
Predicts optimal polynomial coefficients for sparse pseudo-inverse operator.

aarXiv#graph-neural-networks #scientific-computing #fluid-dynamics

research18h

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening

Researchers adapted GotenNet, an equivariant graph neural network, for predicting optical spectra in materials screening. The model improves geometric expressiveness over rotation-invariant scalar features. Evaluations on multiple datasets show the approach enhances prediction accuracy for optoelectronic applications like solar cells. This development could accelerate materials discovery.

Key takeaways

Equivariant graph neural networks improve optical spectra prediction accuracy.
GotenNet adapted for optical spectra prediction task.
Evaluated on multiple datasets for optoelectronic applications.

aarXiv#materials-science #graph-neural-networks #optoelectronics