Dejan.brkic: Dejan.brkic moved page Draft Brkic 538061151 to Brkic et al 2025a

2025-09-02T15:37:38Z

Dejan.brkic moved page Draft Brkic 538061151 to Brkic et al 2025a

Dejan.brkic: Created page with " == Abstract ==
Symbolic regression, a type of machine learning technique, can efficiently disregard variables that are not significant to the final output, even if they w..."

2025-09-02T15:37:35Z

Created page with " == Abstract == <p>Symbolic regression, a type of machine learning technique, can efficiently disregard variables that are not significant to the final output, even if they w..."

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== Abstract ==

<p>Symbolic regression, a type of machine learning technique, can efficiently disregard variables that are not significant to the final output, even if they were initially preselected as inputs. Various input parameters are tested in the three examples presented here, where the outputs are modeled using symbolic regression: estimating the middle plasma torch temperature used for waste gasification, the active energy of a solar power plant, and the diameter of a pipe with a known flow and pressure drop through it. Final highly accurate formulas are produced after numerous attempts with lower performances. The process for rejecting the parameters without or with limited influence is automatic and can be performed without human intervention and supervision. The results obtained using symbolic regression are easily interpretable by human experts. This approach shows how to use machine learning-based modeling as an additional tool for sensitivity analysis.</p>

== Document ==
<pdf>Media:Draft_Brkic_538061151-4053-document.pdf</pdf>

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Brkic et al 2025a - Revision history

Dejan.brkic: Dejan.brkic moved page Draft Brkic 538061151 to Brkic et al 2025a

Dejan.brkic: Created page with " == Abstract == Symbolic regression, a type of machine learning technique, can efficiently disregard variables that are not significant to the final output, even if they w..."

Dejan.brkic: Created page with " == Abstract ==
Symbolic regression, a type of machine learning technique, can efficiently disregard variables that are not significant to the final output, even if they w..."