AI in Scientific Computing

AI in scientific computing uses neural networks to accelerate PDE solvers, build surrogates, and solve inverse problems while respecting physical constraints. Methods include PINNs and operator learning (DeepONet, FNO), with careful loss weighting, numerical stability checks, and error norms compared to traditional solvers.