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arXiv cs.LG
arXiv cs.LG
7/1/2026
Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

Joint discovery of governing partial differential equations from multi-source datasets by competitive optimization

Short summary

Researchers developed MCO-PDE, a competitive optimization framework that discovers physical laws (PDEs) from multi-source datasets by training independent neural surrogates and aggregating consensus coefficients. The method requires as few as 50 observations per dataset and validated on real wave-tank experiments, enabling automated scientific discovery across irregular boundaries.

  • •MCO-PDE uses competitive optimization to discover governing PDEs from heterogeneous data sources
  • •Framework trains independent neural surrogates per dataset and dynamically weights credibility
  • •Validated with minimal observations (50 per dataset) across seven cases and real experiments

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