Data-driven surrogates via regression and CNN-LSTM models with metaheuristic optimization for frequency control in nonlocal porous laminated composite plate resting on elastic foundation
Key Points
Frequency control improved in nonlocal porous laminated composites using advanced models.
Key evidence includes a significant performance enhancement through regression and CNN-LSTM models.
Analysis employs metaheuristic optimization methods to drive model efficiency and accuracy.
Highlights the need for innovative computational approaches in composite material research and applications.
Datenbasierte Surrogate durch Regression und CNN-LSTM-Modelle mit metaheuristischer Optimierung zur Frequenzkontrolle in nichtlokalen porösen laminierten Verbundplatten auf elastischem Fundament | Synapse