Hi-π combines Buckingham Pi dimensional reduction, multi-branch symbolic regression, and polynomial fitting to rediscover physically meaningful dimensionless parameter combinations and to improve symbolic regression of complex formulas.
On physically similar systems; illustrations of the use of dimensional equations[J]
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Hierarchical Dimensionless Learning (Hi-{\pi}): A physics-data hybrid-driven approach for discovering dimensionless parameter combinations
Hi-π combines Buckingham Pi dimensional reduction, multi-branch symbolic regression, and polynomial fitting to rediscover physically meaningful dimensionless parameter combinations and to improve symbolic regression of complex formulas.