2020 has been a landmark year for AI4science. I have had the privilege to work with some of the world’s best experts in a number of challenging scientific domains.
You can read previous posts for other research highlights: generalizable AI (part 1), handling distributional shifts (part 2), optimization for deep learning (part 3), controllable generation (part 5), learning and control (part 6), learning framework (part 7).
Fourier neural operator solves complex PDEs such as turbulent fluid flows, and Orbnet solves quantum chemistry calculations showing 1000x speedups over traditional solvers while maintaining fidelity.
One of the exciting breakthroughs of 2020 is the Fourier neural operator. Neural operators learn mappings from problem specification (e.g. initial and boundary conditions of a PDE) to the solution operator in infinite dimensional spaces. This means that there is no dependence on the resolution or grid of sample points. This allows neural operator to do zero-shot super-resolution, i.e. be able to evaluate at higher resolution and at arbitrary points compared to the training data. None of the previous approaches using deep learning for solving PDEs have this capability.
We show that our method can solve Navier Stokes PDE in the turbulent regime: the first result for a deep learning system. Blog
In a related paper, we proposed an alternative framework for solving large-scale fluid flow problems. Meshfree flownet performs physically-valid super-resolution of fluid dynamics at scale (experiments were run on CORI cluster with 128 V100 GPUs) Project page
Quantum chemistry is the study of chemical properties and processes at the quantum scale. It has been pivotal for research and discovery in modern chemistry. However, as powerful as quantum chemistry has shown itself to be, it also has a big drawback: Accurate calculations are resource-intensive and time consuming, with routine chemical studies involving computations that take days or longer.
We developed Orbnet: a deep-learning based calculator of quantum properties that preserves the fidelity of traditional solvers while obtaining 1000x speed ups. Orbnet combines domain-specific knowledge (molecular orbitals) with the flexibility of deep learning (graph neural networks). This hybrid model allows for transferability to much larger molecules (more than 10x) compared to molecules used for training Orbnet. We also show that Orbnet provides powerful representations for molecular properties and can be directly used for predicting them. News article
Stay tuned for more!
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