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Zhong-Kun Ouyang

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#artificial intelligence Preprint Sep 2026

Human-guided physics-constrained AI agents construct an auditable model of soil-plug evolution

A human-in-the-loop, physics-constrained multi-agent workflow where human experts define admissible physics and modeling boundaries, while agents retrieve evidence, derive equations, implement solvers, and audit the theory-to-code chain is introduced.

Jie Shi, Yi-Min Lu, Zhong-Kun Ouyang · 0 citations

Jul 2026

An AI-Driven Framework for State-Dependent Constitutive Modeling of Soils Integrating Particle Swarm Optimization and Machine Learning

This study develops an AI-driven State-Dependent modeling framework (AI-D-SD-F) that integrates Particle Swarm Optimization and Machine Learning for real-time parameter evolution and adaptive stress-strain simulation and improves prediction accuracy in geotechnical analyses.

Boxiang Zhang, Zhao-Min Lv, Lun-Yang Zhao et al. · Canadian geotechnical journa... · 0 citations