Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
Symbolic regression provides analytical expressions, but it is usually applied one output at a time. This is limiting in process systems, where state variables are often coupled through shared physical parameters. Independent symbolic regression can give accurate individual equations that are difficult to interpret as one model. We present a neuro-evolutionary symbolic regression method for coupled multi-output systems. The method searches for a shared symbolic backbone: a set of latent symbolic units that is discovered once and reused by several outputs through sparse additive or multiplicative read-outs. The discrete model structure is evolved by mutation and crossover, whereas the cont...
| Authors | Manuel Rodriguez |
|---|---|
| Primary category | cs.NE |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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