USFNO-FConvLSTM: Multi-Fidelity Neural Operator Surrogates for Real-Time Hydrostatic Structure Simulation
Abstract
Real-time physics simulation of elastic-coupled hydrostatic cellular structures presents computational challenges that preclude direct numerical methods in interactive applications. This paper proposes USFNO-FConvLSTM, a novel neural operator architecture combining U-Shaped Fourier Neural Operators with Factorized Convolutional LSTM for spatio-temporal surrogate modeling of Adaptive Matrix Ecosystem (AME) structures. We develop a four-level fidelity hierarchy enabling context-appropriate precision: L1 lookup tables (
Fields of Study & Subjects
Model Reduction and Neural Networks
Tensor decomposition and applications
3D Shape Modeling and Analysis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 12 February 2026
- Language
- EN
- Document type
- Preprint