Towards Data Science 8:31 pm on May 27, 2024
The article demonstrates using a PINN with a custom objective to solve differential equations representing electronic circuits driven by sinusoidal sources, achieving accurate solutions compared to analytical methods. It details the network structure and training process, including hyperparameter adjustments for successful outcomes. The neural network's ability to efficiently and effectively model physical phenomena is highlighted.
- PINN Application: Utilizes a Physics-Informed Neural Network (PINN) with custom objectives for solving differential equations in electronics.
- Network Architecture: Consists of input, two hidden layers (GELU activated), output layer; trained using 220 time-domain points.
- Training Process: Involves batch training over multiple epochs with dynamic learning rate decay, and objective plot monitoring for successful training convergence.
- Analytical Comparison: Neural network solutions align closely with analytical results for test cases under different driving frequencies.
- Practical Implications: Showcases neural networks as an effective tool to model and solve complex physical systems efficiently.
https://towardsdatascience.com/physics-informed-neural-network-with-forcing-function-81f59aa24c39
< Previous Story - Next Story >