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Final lecture of our PINNs series.Lectures 1-2 showed what a PINN really is...a neural network uᵩ(x,t) trained by minimizing a loss that contains the PDE residual, plus any data/initial/boundary constraints we have. This finale is just the practical question: when is that a smart tool, and when is it the wrong hammer?1.Use a PINN when labels are scarce but the physics is trusted.2.Use a PINN for inverse problems.You don’t just solve for u...you also learn unknown parameters or forcing terms from
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changing the stiffness length and we will discretize each one of them with the same number of springs determine the stiffness matrix for this problem using the variables above and the provid 36308
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