Differential Equations V – Physics Informed Neural Networks (PINNs)

In our previous posts, we reached the limits of both the classical and computational worlds. Traditional numerical solvers, like Finite Element Methods (FEM) or LSODA are rigorous, but they are computationally crippling. Simulating the hypersonic fluid dynamics of a re-entering spacecraft or forecasting global spatiotemporal weather anomalies can take supercomputers days or weeks. We need [ ]