@JohnLeM is the quant_NOT_always_in_residence expert on SABR :-) I do declare myself not very competent on SABR. I just know how to model it accurately ;) By the way, I did not write the most important part, that is an log-normal process with return to average d \sigma = \kappa(\theta - \alpha) dt ...

And a nice, second order accurate, SABR simulation can be found here. Note however that it is a martingale process used for rates modeling.
Regarding your question, extensions to equity that I know are written as 
dS = (r-q)*S*dt + sigma*S^\beta dW^1
But Alan is surely a better expert than me.

I must say I have a bit of nostalgia for 60 bit supercomputers. I used it to simulate city heating Waterhammer (1st order pde (6), (7)) for The Hague a while back. Punch cards, real line printers, Fortran 66(IV). It might come back again seeing that gas, wood and oil heating will be banned soon. No...

Mc Ghee is outrunned, NNs compute today 100 millions times faster than ODE methods !!!
https://arxiv.org/pdf/1910.07291.pdf

// BTW kernels can be characterised a being univeral, characteristic, translation-invariant, strictly positive-definite, What's that? Stricly positive kernels on [$]\Omega[$] are functions [$]k(x,y)[$] satisfying [$]k(x^i,x^j)_{i,j \le N}[$] is a s.d.p matrix for any set of distinct points [$]x^i \...

For me it's perfectly clear what the article is saying. After reading one of their  included reference , it is now more clear. I was unable to understand their log-entropy functional (3) without this reading. Frankly, they could have developed a little bit more to make it understandable, or at leas...

JohnLeM, Looks like those kernels (and RKHS) that you mention have many applications. Can we say that kernels allow us to define metrics and norms on probability measures? Then you can use the artillery of Functional Analysis bear, as I attempted to introduce before it was shot down. People like th...

I tried again one hour this morning. But it is really unclear. However one of the included reference seems more detailed and clear, I'll try to fall back to it to understand their work.

A loss surface of a neural network can approximate anything. Including cows. This paper seems quite interesting but I spent unsuccessfully two hours trying to understand it. But nice cows indeed ! Is the experience kathartic? I am not sure if it is cathartic or not. I will try again to see if I can...

A loss surface of a neural network can approximate anything. Including cows.

This paper seems quite interesting but I spent unsuccessfully two hours trying to understand it. But nice cows indeed !

Another one..

https://arxiv.org/abs/1910.10262

Cute ODE (10)

Another one ... bite the dust ?
mmm...I am really feeling some kind of embarrassments when I look to all these DL papers that will revolution PDE methods.

I see. Meantime, I hear more and more voices warning that billions of public and private money are wasted in a technology without any foundation, thus inefficient. The last time that this legitimacy problem popped up, the artificial intelligence community crossed a 15 years desert as nice as your p...

It starts to sound like a married couple's argument. I'm outta here.

@Cuchullain, it seems that you put your finger exactly where it hurts ...

Correct. Too bad, maybe a little bit of good will could help ? For me the topic is good, if I understood it well : "dig in Cybenko Theorem to turn it into a practical tool". This could really help the IA community to understand their tools. Or, equally fairly, it could help the numerical optimisati...

I see. Meantime, I hear more and more voices warning that billions of public and private money are wasted in a technology without any foundation, thus inefficient. The last time that this legitimacy problem popped up, the artificial intelligence community crossed a 15 years desert as nice as your pi...