Two-stage pretraining for chemicals:

1. Masked language model
2. Predict chemical properties

@omendezlucio Nicolaou, @bertonearnshaw

https://arxiv.org/abs/2211.0265

We're happy to announce that our paper "Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils" is published now in "Computers & Fluids" and can be enjoyed at: https://authors.elsevier.com/a/1g3shAQO4pqSu , congrats Liwei

The source code is also available at: https://github.com/tum-pbs/coord-trans-encoding

The trained neural network yields results that resolve all necessary structures such as shocks, and has an average error of less than 0.3% for turbulent transonic cases.

Our paper on adversarially-robust regression was accepted to SaTML 2023 (https://satml.org) -- the first ever IEEE Conference on Secure and Trustworthy Machine Learning!

I'm really excited about this conference and hoping to see it take off. There's so much important work to do in this area.
#SaTML #AdversarialML

We recently put out a position paper titled "Neurosymbolic Programming for Science"
https://arxiv.org/abs/2210.05050

This position is informed by our experience collaborating with scientists: science is an iterative process of analyzing data, proposing hypotheses, and conducting experiments. Because scientists reason more readily in symbolic terms, it is important to develop frameworks that natively inherit the both the flexibility of neural networks and the rich semantics of symbolic models.