Equivariant neural operators for gradient|consistent topology ...

Equivariant neural operators for gradient-consistent topology ...

We introduce a novel NO architecture called U-Net Fourier neural operator (U-Net FNO), which combines the multi-resolution properties of U-Nets with the ...

Equivariant neural operators for gradient-consistent topology ...

Most traditional methods for solving partial differential equations (PDEs) require the costly solving of large linear systems. Neural operators (NOs) offer ...

Equivariant Neural Operators for Gradient-Consistent Topology ...

Request PDF | Equivariant Neural Operators for Gradient-Consistent Topology Optimization | Most traditional methods for solving partial differential ...

Equivariant neural operators for gradient-consistent topology ...

Article on Equivariant neural operators for gradient-consistent topology optimization, published in Journal of Computational Design and Engineering 11 on ...

Equivariant neural operators for gradient-consistent topology ... - OUCI

Abstract Most traditional methods for solving partial differential equations (PDEs) require the costly solving of large linear systems. Neural operators ...

[2405.15429] E(n) Equivariant Topological Neural Networks - arXiv

Abstract:Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and ...

E(n) Equivariant Topological Neural Networks | by Claudio Battiloro

Graph neural networks are limited in handling higher-order interactions. Topological Deep Learning (TDL) offers a powerful solution leveraging ...

Lie Algebra Canonicalization: Equivariant Neural Operators under...

The quest for robust and generalizable machine learning models has driven recent interest in exploiting symmetries through equivariant ...

Geometric deep learning and equivariant neural networks

We develop gauge equivariant convolutional neural networks on arbitrary manifolds using principal bundles with structure group K and equivariant ...

E(n) Equivariant Topological Neural Networks - arXiv

This paper introduces E(n)-Equivariant Topological Neural Networks (ETNNs), which are E(n)-equivariant message-passing networks operating on ...

10. Equivariant Neural Networks

Equivariant neural networks are part of a broader topic of geometric deep learning, which is learning with data that has some underlying geometric ...

Theory for Equivariant Quantum Neural Networks

As a special implementation, we show how standard quantum convolutional neural networks (QCNNs) can be generalized to group-equivariant QCNNs ...

Vector Neurons: A General Framework for SO(3)-Equivariant Networks

convolutional neural networks by extension operators. arXiv preprint arXiv:1803.10091, 2018. 2, 6. [3] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton ...

Equivariant deep learning for 3D topology optimization

The third publication showcases the efficacy of equivariant neural networks and a physics-inspired data preprocessing strategy, substantially reducing the need ...

Topological Neural Networks go Persistent, Equivariant, and ...

Fourier neural operator for paramet- ric partial differential equations. ... consistent with (Immonen et al., 2023). For the continuous ...

A topological model for partial equivariance in deep learning and ...

Group equivariant neural networks are the models whose structure is restricted to commute with the transformations on the input. Therefore, it is important to ...

bitzhangcy/Neural-PDE-Solver - GitHub

Group equivariant Fourier neural operators for partial differential equations. ICML, 2023. paper ... neural networks for topology-consistent models. JCP ...

A General Framework for Equivariant Neural Networks on Reductive ...

As Lie algebra are vector spaces, the functorial equivalence between the two categories of representation translates geometrical and topological questions into ...

Equivariant Neural Networks for Indirect Measurements

To this end, we build appropriate network structures by developing layers that are equivariant with respect to data transformations induced by well-known ...

General framework for E(3)-equivariant neural network ... - Nature

The combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, ...