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Zhen-Tao Tan

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#artificial intelligence Preprint Sep 2026

PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs

Physical trajectories contain more than snapshots of a system: they also reveal how its states evolve under governing conditions. However, representation learning for parametric partial differential equations (PDEs) has largely relied on reconstruction-based objectives that emphasize recovering observed physical fields...

Zhen-Tao Tan, Jian-Rong Zhang, Rui-Jie Quan et al. · 0 citations

Preprint Aug 2026

From Points to Edges: Edge-Conditioned Spectral Operators for Physics-Sensitive PDE Learning

Neural operators have become a central tool for solving partial differential equations (PDEs), with spectral operators offering efficient global mixing across spatial locations. However, many PDEs contain physics-sensitive local structures that are critical to the underlying physical behavior. For example, in Darcy flo...

Zhen-Tao Tan, Rui-Jie Quan, Yi Yang · 0 citations