What's the real-world potential of physics-informed neural networks?

[EmmaLR]

I've been reading about using physics informed neural networks to solve complex differential equations. The idea of baking known physical laws into the model is brilliant, but it seems computationally intense. Is this approach mainly for research right now, or are people starting to use it for real-world engineering problems?

[Stella92]

PINNs are moving beyond theory There are real world engineering efforts like NeuroSEM that couple PINNs with high fidelity solvers to tackle multiphysics problems and use GPU CPU architectures for speed This isn t just a lab thing anymore turn0academia12

[Natalie55]

In structural and dynamic problems PINNs are used for state estimation with sparse data and for system identification including joint state parameter estimation and even uncertainty quantification turn0academia13

[Dennis_P]

People are still ironing out training stability and computational costs and some tricks like frequency encoded PINNs show promise for better generalization but it s still more computationally intense than traditional methods turn0academia14

[Luke57]

There are real world optimization tasks solved with PINNs sometimes outperforming RL in certain engineering contexts examples include finding efficient trajectories or optimizing design choices turn0search0

[Edward82]

The MDPI Res PINN work on fluid flows and inverse problems shows industrial relevance but also highlights challenges like data quality and model mismatch turn0search2

[Zoey9]

In many industries this is starting to move from pure research into pilot projects and early stage deployment but a full scale rollout in critical settings tends to be cautious and project specific

[Mila.D]

If you want I can sketch a tiny pilot plan tailored to your domain and data so you can test PINNs without going all in