<ul><li>A new thermodynamics-informed latent space dynamics identification framework, tLaSDI, has been proposed for modeling parametric nonlinear dynamical systems.</li><li>The framework combines autoencoders for dimensionality reduction with parametric GENERIC formalism-informed neural networks (pGFINNs) to efficiently learn parametric latent dynamics while upholding thermodynamic principles like free energy conservation and entropy generation.</li><li>A physics-informed active learning strategy is included to improve model performance through adaptive sampling of training data based on a residual-based error indicator, resulting in better outcomes than uniform sampling at the same computational cost.</li><li>Numerical experiments on different equations demonstrate that the proposed method achieves significant speed-up, reduced relative errors, and lower training and inference costs, while also providing insights into the thermodynamic behavior of the system through learned latent space dynamics.</li></ul>

Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems

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