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arXiv AI By Agung Nugraha, Heungjun Im, Jihwan Lee

Partial Inverse Design of High-Performance Concrete Using Cooperative Neural Networks for Constraint-Aware Mix Generation

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The paper introduces a cooperative neural network framework for partially inverse designing high‑performance concrete (HPC) mixes. It combines an imputation model with a surrogate strength predictor and is trained cooperatively, enabling it to produce valid, performance‑consistent mix designs in a single forward pass without retraining for different constraints. Compared to baseline methods, the approach achieves higher strength consistency (R² 0.84–0.89) and reduces mean squared error by 42–60%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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