Work

Geometry-informed neural operator transformer for partial differential equations on arbitrary geometries

Year: 2025

Type: article

Abstract: Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiring repeated evaluations of partial differential equations. This work introduces the Geometry-Informed Neural Operator Transformer (GINOT), which integrates the transformer architecture with the neural operator framework to enable forward predictions on arbitrary geometries. GINOT employs a sampling and grouping strategy together with an attention mechanism to encode surface point clouds that are unordered, exhibit non-uniform point densities, and contain varying numbers of points for different geometries. The geometry information is seamlessly integrated with query points in the solution decoder through the attention mechanism. The performance of GINOT is validated on multiple challenging datasets, showcasing its accuracy and generalization capabilities for complex and arbitrary 2D and 3D geometries.

Source: Computer Methods in Applied Mechanics and Engineering

DOI: 10.1016/j.cma.2025.118668

Authors: Qibang Liu, Weiheng Zhong, Hadi Meidani, Diab Abueidda, Seid Koric, Philippe Geubelle

Institutions: University of Illinois Urbana-Champaign, Kansas State University, National Center for Supercomputing Applications, New York University Abu Dhabi

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,830

Funders: National Science Foundation, University of Illinois at Urbana-Champaign