Publications (7)
UniSymNet: A Unified Symbolic Network Guided by Transformer
Xinxin Li, Juan Zhang, Da Li +3
Symbolic Regression (SR) is a powerful technique for automatically discovering mathematical expressions from input data. Mainstream SR algorithms search for the optimal symbolic tr…
Weak-PDE-Net: Discovering Open-Form PDEs via Differentiable Symbolic Networks and Weak Formulation
Xinxin Li, Xingyu Cui, Jin Qi +3
Discovering governing Partial Differential Equations (PDEs) from sparse and noisy data is a challenging issue in data-driven scientific computing. Conventional sparse regression me…
YingLong-weather: AI-Based Limited Area Models for Forecasting of Non-precipitation Surface Meteorological Variables
Pengbo Xu, Xiaogu Zheng, Tianyan Gao +9
Recently, artificial intelligence-based (AI-based) models for forecasting of global weather have been rapidly developed. Most of the global models are trained on reanalysis dataset…
Blow-up criteria for Boussinesq system and MHD system and Landau-Lifshitz equations in a bounded domain
Jishan Fan, Wenjun Sun, Junping Yin
In this paper, we prove some blow-up criteria for the 3D Boussinesq system with zero heat conductivity and MHD system and Landau-Lifshitz equations in a bounded domain.
ViSymRe: Vision Multimodal Symbolic Regression
Da Li, Junping Yin, Jin Xu +2
Extracting interpretable equations from observational datasets to describe complex natural phenomena is one of the core goals of artificial intelligence. This field is known as sym…
EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification
Da Li, Xinxin Li, Xingyu Cui +3
Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulati…