תכנון חלקי של סימנטיקה גבוהה של סיליקט באמצעות רשתות עצביות שיתופיות ליצירת מערכת תכנון

תקציר מקורי באנגליתarXiv:2512.06813v3 Announce Type: replace Abstract: High-performance concrete (HPC) requires complex mix design decisions involving interdependent variables and practical constraints. While data-driven methods have improved predictive modeling for forward design in concrete engineering, inverse design remains limited, especially when some variables are fixed and only the remaining ones must be inferred. This study proposes a cooperative neural network framework for the partial inverse design of HPC. The framework integrates an imputation model with a surrogate strength predictor and learns through cooperative training. Once trained, it generates valid and performance-consistent mix designs in a single forward pass without retraining for different constraint scenarios. Compared with baselin