arXiv Machine Learning
ScaGNN: a Graph Neural Network for Multiple Scattering Simulations
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 15
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
arXiv:2609.14841v1 Announce Type: new Abstract: Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability w...
arXiv Machine Learning
Jul 14
A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries
arXiv:2607. 11672v1 Announce Type: new Abstract: Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs.
Hugging Face Trending Papers
Jul 13
A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries
Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data.
arXiv Machine Learning
Jun 5
Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains
arXiv:2604. 09361v3 Announce Type: replace Abstract: This paper introduces the Stochastic-Dimension Frozen Sampled Neural Network (SD-FSNN), a novel computational framework for solving high-dimensional Gross-Pitaevskii equation (GPE) on unbounded domain.
arXiv Machine Learning
Jul 31
Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries
arXiv:2605. 00760v2 Announce Type: replace Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network, the DeepONet framework.
arXiv Machine Learning
Sep 10
Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment
arXiv:2609.07983v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a me...