One physics-informed network trained on the heat equation, not labeled data, predicts laser-scan temperature fields for unseen alloys including copper with ~1% relative error.
Leveraging simulated and empirical data-driven insight to supervised-learning for porosity prediction in laser metal deposition.Journal of Manufacturing Systems, 62:875–885
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Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework
One physics-informed network trained on the heat equation, not labeled data, predicts laser-scan temperature fields for unseen alloys including copper with ~1% relative error.