DM me , f500 company with lots of interesting ML projects. Let's talk

One major problem is not enough diversity in ML/DL research. I don't mean this from a social sense only. Most of the major development is lead by FAANG or research labs doing FAANG'ish work but real ML/DL work doesn't have trillion token datasets or fuss free gpu budgets. Industrial AI for egs is a field which is underperforming compared to advances in certain sciences and general b2C areas like NLP or recsys.

Even computer vision long thought to be solved still struggles in many applications to provide great solutions for example in segmentation of artifacts in.used catalysts .

We need more folks from industrial/ real life areas working with ML on small data/ extreme sparse phenomena or complex natural science systems in an interdisciplinary sense.

On a completely different but related note . If you look at ML for climate change, it's so far from what's required to actually make a change in climate. Stuff like using NLP for catalysts or conv lstm for weather like metnet from Google makes for great PR, but it's useless in the greater scheme of things. None of those ideas get us to developing and shipping climate tech related solutions in the short term. Perhaps if we had more multidisciplinary teams both in research as well as in management, because decision is made by non tech folks in general, we might have much better outcomes.

Narrow AI still has tremendous potential to change our world for the better. We are in the early stages of Cambrian explosion era of narrow AI is what I feel

Try deepOnets, but honestly without knowing specifics of your problem. It is hard to advise, PiNN is different from classic DL

PiNN aren't really supervised or unsupervised so to speak. It's a misleading way to think about PiNN architecture

Neural pde solvers can be of three flavours - operator learning, graph pde and purely function approximator ( lagaris 2007 ) approach.

SOTA in pinns is a bit useless. Nobody cares if you can do burgers equation as fast as possible. Real life systems are coupled, mixture of pde/ ode , possibly stiff, it's a smorgasbord of challenges.

Fno works great in some situations but it has limitations in handling stochastic multiscale systems - think high RANS

When it comes to PiNN ymmv

Physics informed neural networks is huge area of research in applied engineering, quantum chemistry and physics.

Three major schools of approach are

1. Function approximation - originating from lagaris et al
2. Operator learning - karniadikis (deepOnets) and Caltech ( Fourier neural operator l) FNO is getting more adverts due to Nvidia trying to make it 'the' model
3. Graph neural network approach - Battaglia et al .. this is primarily used for studying problems framed as large scale interactive systems of X where X is particles , objects

We do active work in this space