Papers
1–3 of 3Research Paper·Feb 25, 2026
NESTOR: A Nested MOE-based Neural Operator for Large-Scale PDE Pre-Training
Neural operators have emerged as an efficient paradigm for solving PDEs, overcoming the limitations of traditional numerical methods and significantly improving computational efficiency. However, due ...
5.0 viability
Research Paper·Mar 18, 2026
RHYME-XT: A Neural Operator for Spatiotemporal Control Systems
We propose RHYME-XT, an operator-learning framework for surrogate modeling of spatiotemporal control systems governed by input-affine nonlinear partial integro-differential equations (PIDEs) with loca...
4.0 viability
Research Paper·Mar 18, 2026
Translation Invariance of Neural Operators for the FitzHugh-Nagumo Model
Neural Operators (NOs) are a powerful deep learning framework designed to learn the solution operator that arise from partial differential equations. This study investigates NOs ability to capture the...
4.0 viability