4 papers · 1 filter
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…
Circuit-Inspired High-Order Neural Networks with Unified Neural Dynamics Modeling for PDE Solving and Visual Perception
Tongfei Chen, Jingying Yang, Linlin Yang +8
Deep networks often rely on architectural heuristics to shape representation evolution, limiting their ability to model data governed by intrinsic dynamics. We present the Circuit-…
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…
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…