1 citations · 1 across the 34 of their papers we have counts for
4 papers · 2 filters
HERO: History-Enriched Rollout Training for Long-Horizon Autoregressive Neural Operators
Jiaquan Zhang, Shuxu Chen, Haifan Meng +6
Neural operators provide fast surrogates for time-dependent partial differential equations (PDEs) by applying a learned evolution operator recursively to its own predictions, but t…
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…