Jan Drgona

Data scientist
I am a data scientist at Pacific Northwest National Laboratory. My current research interests fall in the intersection of model-based optimal control, constrained optimization, and machine learning.
From my understanding there are several open challenges with using neural nets to approximate numerical physics-based simulators.Β 

As neural nets and are in general interpolating machines they perform well in high data regimes with well sampled parameter distributions. Generalization problems may arise in low data regimes or with significant distribution shifts between train/test datasets.

In the case of PINNs, you aim to improve the generalization by augmenting the loss with extra soft constraint penalties on known physics. This is appealing but it does not provide guarantees that the physics will hold. Thus some open challenges include dealing with hard constraints, or designing correction schemes to mitigate the errors of the neural surrogates in long-term simulations.

Great post and paper ο»Ώ Martin RΓΆck ο»Ώ!
Lots of data is always tempting for AI researchers πŸ˜€
Please send me PM and I can suggest you couple of names from the academia and national labs who might be a good fit for the collab on this particular problem.
Dear Sumeet,Β if you are still searching for collaborators, feel free to fish in the CCAI directory where you can search for individuals with specific expertise: https://directory.climatechange.ai/
Certainly we can put this paper in the queue.