TL;DR
This study proposes a method for predicting dynamic responses based on an enhanced physics-informed gated recurrent unit (EPIGRU) neural network that outperforms conventional GRU and PIGRU across different data splits, and reduces computation time by 91%.
The results demonstrate that incorporating adaptive loss balancing into the physics-informed BP neural-network framework can improve roll motion prediction accuracy without introducing a substantial additional computational burden.
A public high-frequency experimental dataset covering diverse operating conditions is adopted and the proposed framework provides an effective data-driven approach for friction hysteresis prediction and offers potential support for nonlinear modeling and digital twin applications.
This study develops a unified physics-constrained deep reinforcement learning framework for OpenSees Steel02 and DowelType identification, giving accuracy comparable with tuned PSO at the same online OpenSees-call budget while retaining a reusable learned initialization step.
Grounded in the insight that temporal position sequences implicitly encode underlying dynamics, HiLNN employs a recurrent encoder to extract a latent context from history that not only reconstructs the unobserved initial velocity but also adaptively modulates the mass matrix, potential energy, and damping coefficients...
The Physics-Informed Stochastic Configuration Machine is proposed, a novel backpropagation-free framework for both forward and inverse problems in differential equations that achieves high-fidelity predictive accuracy and robust parameter identification while accelerating the training process by orders of magnitude com...
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