Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression

Understand

Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior.

• Symbolic regression is a method that can automatically generate such models from data.
• Historically, symbolic regression has been predominantly realized by genetic programming, a method that evolves populations of candidate solutions that are subsequently modified by genetic operators crossover and mutation.
• However, this approach suffers from several deficiencies: it does not scale well with the number of variables and samples in the training data - models tend to grow in size and complexity without an adequate accuracy gain, and it is hard to fine-tune the model coefficients using just genetic operators.