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Found 3 papers, 1 papers with code
An invariance constrained deep learning network for PDE discovery
However, the discovery of governing equations from sparse data with high noise is still very challenging due to the difficulty of derivatives computation and the disturbance of noise.
Discovering Governing Equations by Machine Learning implemented with Invariance
The partial differential equation (PDE) plays a significantly important role in many fields of science and engineering.
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
A universal rule-based self-learning approach using deep reinforcement learning (DRL) is proposed for the first time to solve nonlinear ordinary differential equations and partial differential equations.
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