A diagram of a GA-Optimized Physics-Informed Neural Network for Manufacturing Digital Twin. Panel A: Physical Manufacturing Process. A laser beam heats a melt pool on a physical manufacturing asset. An infrared camera and thermocouple stream sensor data to a system for streaming data assimilation. Panel B: GA-Optimized PINN Framework. A genetic algorithm meta-optimization process involving initialization, selection, crossover, and mutation optimizes loss weights. These optimized loss weights are used in a Physics-Informed Neural Network surrogate model, which includes data loss, physics residual loss, and boundary loss components. Panel C: Digital Twin Outputs. The outputs include full-field temperature prediction, monitoring over time, anomaly detection, and control feedback.