This paper introduces a physics-informed neural network that uses Deep Sets to encode sensor data, allowing one model to adapt to new parameters and boundary conditions without retraining, with tests on a chaotic ODE, flow past a cylinder, and real composite plate heating.
International Journal of Heat and Mass Transfer 217, 124671 (2023)
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Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks
This paper introduces a physics-informed neural network that uses Deep Sets to encode sensor data, allowing one model to adapt to new parameters and boundary conditions without retraining, with tests on a chaotic ODE, flow past a cylinder, and real composite plate heating.