mishurov

Apple consumerist NPC followers will find excuses for it anyway.

They wrote a book based on that course and notes. https://www.cs.ubc.ca/~rbridson/fluidsimulation/

It's for computer graphics. Fluid simulation for, for example, simulating air pressure on an aircraft in development is more precise and numerical methods are more complex.

For using a numerical solver e.g. Runge-Kutta you need nothing to master, statistics operations master.

It's actually a nifty idea, could be implemented as something like blockchain in terms of distribution and syncing diffs among peers.

How will Linux ever recover? I want kernel to be based and red-pilled.

this. I was just about to write that the tutorial should be shown to every Electron developer.

GPU accelerates training time considerably. For development it's very convenient to use a Linux laptop with nVidia Optimus. I was able to train a quite complex model on laptops's nVidia (optirun process) while continue working using Intel SOC's GPU. However GPU memory is crucial it's better to choose a laptop with 4GB or more video RAM.

He's a great teacher but I prefer to see linear algebra from the point of view of linear maps/operators/transformations and matrices as their representations. His approach goes rather from matrix arithmetic as far as I know.

Makes inappropriate advances.

There're lots of different GI methods, most of them use spatial acceleration structures such as BVH and KD-Trees after triangulation. Fluids use a grid for discretisation, behind the scenes the PDEs are solved be means of the finite difference methods, it has nothing to do with ray intersection tests.

It is a sort of geometric optics approximation as I understand, otherwise it was an electromagnetic or quantum field. It looks more for entertainment than for research purposes.

It's a typical raytracing / pathtracing pipeline. Ray intersection accelerated via BVH or KD-tree and computations of BxDF at intersection points.

It would be great if they make an API for the ray intersection routines outside Microsoft's DirectX it can be very useful in post-production renderers.

It's definitely a shorthand since a typical solution for collision detection is a space partitioning recursive data structure which reduces collision tests to O(n log n)

I appreciate the work done at the hardware level for streaming computations of structured data. But from the high level view, it's more critical to be able to compute efficiently matrix decomposition, it helps to solve systems of linear equations and obviously discretised PDEs.

Wow, so progressive.

What about the real world mathematical and computational problems?

"Meep (or MEEP) is a free finite-difference time-domain (FDTD) simulation software package developed at MIT to model electromagnetic systems. Meep used Haskell to generate C++ code, after Meep 1.0 Haskell generation droped in favor of handwritten C++ code."

Alright then.

>High-velocity

>Hamilton

>This approach should prove useful for producing novel quantum states of light and quantum entanglement on demand.

I'd rather say that machine learning is an application of mathematical statistics.

tfw you fell into the machine learning meme

Well, it's a much more interesting idea than a usual hype article with yet another application of the statistical inference.

I was very surprised when I'd discovered that the assembly in the Linux kernel sources for x86 was written in the AT&T syntax and the assembly for ARM was not. I always thought that the AT&T syntax was supposed to be independent from an architecture.

Why don't people use AT&T syntax for ARM?

vim

vim