A sequential PINN predicts multi-track LPBF temperatures with about 2.5 percent mean error and trains 8.5 times faster than a PI-DeepONet alternative.
Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks
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Physics-Informed Surrogates for Temperature Prediction of Multi-Tracks in Laser Powder Bed Fusion
A sequential PINN predicts multi-track LPBF temperatures with about 2.5 percent mean error and trains 8.5 times faster than a PI-DeepONet alternative.