4 papers · 1 filter
LieDiscover: Adaptive Symbolic Library Construction for Explicit Open-form Symmetry Discovery
Xinxin Li, Jianming Ma, Xingyu Cui +3
Discovering underlying symmetries from data has emerged as a crucial challenge in scientific discovery. Existing data-driven methods for symmetry discovery fail to determine the ex…
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…
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-…