10 citations · 10 across the 20 of their papers we have counts for
4 papers · 1 filter
TF-SNO: Time-Frequency Gated Spectral Neural Operators for Learning Non-Stationary Partial Differential Equations
Yitian Zhou, Chaoning Zhang, Zhenzhen Huang +8
Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution can drift over time. While…
Autoregression-Free Neural Operators for Time-Dependent PDEs
Jiaquan Zhang, Caiyan Qin, Haoyu Bian +7
Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equations (PDEs). For time-dependent…
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
Jiaquan Zhang, Fachrina Dewi Puspitasari, Songbo Zhang +7
Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. E…
Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
Zhenzhen Huang, Haoyu Bian, Jiaquan Zhang +6
Several complex physical systems are governed by multi-scale partial differential equations (PDEs) that exhibit both smooth low-frequency components and localized high-frequency st…