Arvind Mohan

Hell yeah! I wore Metallica yesterday and Opeth the day before. Next AGU should we all have a metalhead social?

Ok total stranger here and this popped in my feed… but as someone rocking a Gojira tee at AGU, I had to represent 🤘

At @agu.org with great colleagues and interesting work. And snarky badge stickers, courtesy of our very own US Dept of Energy 🙃

I’ve mountain biked around the rim of those lakes. To this day, the most surreal place I’ve been in! And the people were so genuinely warm.

Fascinating. Can you say more? All I’ve seen online is either hype or outright dismissal of its capabilities. But I’m cautiously optimistic.

Or more accurately, discussing numerical methods for gradient descent while undergoing (rapid) gradient descent ⛷️

I'm really excited about this paper. Some context for 🔭🧪🔬 folks, as the AI summary may be a bit dry...

A common activity in scientific ML is to train AI models on numerical data, generated by simulation. The simulation data is assumed to be ground truth... 🧵

Cool article by @marccoru.bsky.social et al. exploring the use of spherical harmonics and very shallow SIREN networks to convert longitude and latitude meaningful geospatial embeddings on the sphere (code is also available) arxiv.org/abs/2310.06743

The Big Lebowski!!

This is a fantastic resource to make research more accessible!

To add, even in cases where extrapolation is seen, its likely because of "lucky" interactions in numerical dissipation between the discretization scheme, the grid and the initial condition. Our paper provides formal tools to a priori estimate error and identify extrapolation limits *before* training

Just joined - Where the sky is still blue, but the bird is long gone.