arXiv (OAI Expanded)
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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling
Recent advances in physics-augmented neural networks have enabled thermodynamically consistent data-driven constitutive modeling of complex inelastic materials. Most existing approaches, however, implicitly adopt a specific thermodynamic framework and embed structural assumptions such as normality,
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