An attention-based physics-guided CNN surrogate is trained to predict long-time microstructural evolution under the Cahn-Hilliard equation for both critical and off-critical mixtures while preserving composition and matching Lifshitz-Slyozov domain growth.
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RL agent learns optimal excitation signals for Quanser Aero 2 parameter identification, achieving competitive accuracy on three parameters with 0.75% safety violations and outperforming classical baselines.
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Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics
An attention-based physics-guided CNN surrogate is trained to predict long-time microstructural evolution under the Cahn-Hilliard equation for both critical and off-critical mixtures while preserving composition and matching Lifshitz-Slyozov domain growth.
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Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
RL agent learns optimal excitation signals for Quanser Aero 2 parameter identification, achieving competitive accuracy on three parameters with 0.75% safety violations and outperforming classical baselines.