I want to use PINN to solve those theoretical equations in quantitative remote sensing. But what I'm not sure about is whether PINN can solve a series of nested (or serial) physical equations.
Physics-Informed Neural Networks (PINN) can indeed be applied to solve theoretical equations in quantitative remote sensing. PINN are a type of neural network that can incorporate known physical laws or constraints into their architecture, making them particularly suitable for problems in physics-based fields like remote sensing. In the context of remote sensing, where the underlying processes are governed by physical laws, PINN can be used to solve a series of nested or serial physical equations. By incorporating the physics of the remote sensing process into the neural network's training, PINN can learn to accurately model the relationships between input data (e.g., sensor measurements) and the desired output (e.g., environmental parameters). However, it's important to note that the effectiveness of PINN in solving nested or serial physical equations in remote sensing would depend on various factors, such as the complexity of the equations, the availability of sufficient training data, and the network architecture. Proper experimentation and validation would be necessary to determine the feasibility and performance of using PINN for this purpose.
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