Today, we are bringing other exciting results involving black holes and AI. We released a new paper:

"Black Hole Weather Forecasting with Deep Learning: A Pilot Study"

Work by Roberta Duarte (@import_robs), Rodrigo Nemmen (@nemmen) and João Paulo Navarro (from @NVIDIABrasil).

The authors used deep learning to simulate the dynamics of gas accreting onto a black hole, i.e., black hole weather forecasting.

They trained the model (U-Net) with frames from numerical solutions of the hydrodynamical equations.
Numerical simulations are time-consuming. A simple simulation can take as long as 7 days to finish. If we go with more complex simulations, this time may increase.

We want to investigate if deep learning can be a new method to simulate accurately in less time!
In the paper, they discussed two examples:

1- The model simulating only one system after learning only from this system
2- The model simulating an unseen system after training with several systems with different initial conditions
In the first example, they trained the model with a single system and analyzed how the model simulates by iterative predictions.

The result is that the model can simulate up to 8e4 gravitational time accurately with a speed-up of 30000x faster!
In the 2nd example, they fed the model seven different simulations with the same physics but other initial conditions. They informed the model how the initial conditions differ from one to another.

However, they hid one system to understand the generalization power of the model!
They analyzed how the model can simulate an unseen system only by looking at previous systems!

It simulated the unseen system for 4e4 gravitational time, showing that the model can generalize the black hole physics presented in the dataset!
In the second example, the model can also simulate the systems it learned from:
For more details, please check it out on arXiv: https://t.co/VBh3RQnhov

More from Science

Ever since @JesseJenkins and colleagues work on a zero carbon US and this work by @DrChrisClack and colleagues on incorporating DER, I've been having the following set of thoughts about how to reduce the risk of failure in a US clean energy buildout. Bottom line is much more DER.


Typically, when we see zero-carbon electricity coupled to electrification of transport and buildings, implicitly standing behind that is totally unprecedented buildout of the transmission system. The team from Princeton's modeling work has this in spades for example.

But that, more even than the new generation required, runs straight into a thicket/woodchipper of environmental laws and public objections that currently (and for the last 50y) limit new transmission in the US. We built most transmission prior to the advent of environmental law.

So what these studies are really (implicitly) saying is that NEPA, CEQA, ESA, §404 permitting, eminent domain law, etc, - and the public and democratic objections that drive them - will have to change in order to accommodate the necessary transmission buildout.

I live in a D supermajority state that has, for at least the last 20 years, been in the midst of a housing crisis that creates punishing impacts for people's lives in the here-and-now and is arguably mostly caused by the same issues that create the transmission bottlenecks.

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