Blue noise is best for situations where you only have the budget for 1 or 2 samples per pixel. The very low sample counts is where it performs considerably better than white noise. If you integrate with many samples, tens or hundreds or more, then the advantage of blue noise over other “colors” diminishes.
The author of this post has some examples in other articles on his blog, for example using blue noise vs white noise for ray traced soft shadows. https://blog.demofox.org/2020/05/16/using-blue-noise-for-ray...
> As a chemist, I always model real-world noise with a gaussian distribution
Gaussian distribution is what you get when you have a lot of independent random events adding up (it's the central limit theorem), so it applies a lot in real life but it can also cause modeling issues if it's not how randomness appears in your system. (Please note that I have absolutely no knowledge on chemistry so I have no idea if it's something that could be relevant in your field)
https://youtu.be/Ge3aKEmZcqY?t=965
So if you use white noise, it looks unnaturally clustered with patches. This is the part that discusses the noise.
The energy profiles for all of these scenarios is empirically determined and "well known" in the community.
The ex helped model the "death star" auditorium space at the academy of motion picture arts and sciences in LA. IIRC it sounds really aquarium-ey during normal times and during events they roll out strategically placed dampeners.
- Deciding what ratio to mix things in for the countless liquid products that are combinations of already-discovered chemicals. Everything at the grocery store that comes in a jug or a bottle (cleaning products, hair conditioner, drain cleaner) falls in to this category.
- Doing industrial research to improve existing processes. This would include discovering new catalysts, and trying out the endless permutations of solvents and conditions in which existing reactions take place, to optimize them for whatever the biggest operating costs are.
- Figuring out how to recycle industrial chemicals and get the valuable stuff back out of sludge and effluent. This is a surprisingly big field with important consequences.
- Working on specialty materials, like plastics and synthetic rubbers, that are not completely dissimilar from existing products but require a chemist to design them for specific, demanding applications.
The fact that everything involving the physical world gets you paid way less for work that's way more difficult than programming will come back to bite us somehow, but it's hard to say when.
That just reads as “I am better than you”.
Programming is arbitrarily difficult, and it rarely has an objective reality to measure against. I have some kind of “Peter Principle” in mind where people continuously reach their own personal limits of complexity. However they then expand their skills in an ill-defined problem space to level up and beat their prior limits. Programmers display a massive variety of talent, and most of the talent is invisible because the problem space is heinously deeply complex and outside observers only see glimmers on the surface.
Or is your implication that idiots and/or lazy people choose to be programmers? Certainly there is a share of them in the discipline too: perhaps smart for chasing money and perhaps even content with the challenges they face personally.
Regardless, you are being very judgmental towards programming.
Your own descriptions of your discipline could be perceived as rote work or make work, on the same level as making another CRUD app for a business, that doesn’t require deep applied intelligence. How people apply their skills and intellect to the problem defines how hard the problem is.
Then I think of https://library.sciencemadness.org/library/books/ignition.pd... which I read as a study in applied intuition in Chemistry at a genius level (with some survivor bias to boot!).
In software, there's no such thing as marginal cost, the only tool you need is a $300 laptop, and all you need to know can be read for free online. That's an amazing world, from an economic standpoint, and it's no surprise at all that it's so much easier to make money that way.
I think what makes people want to become natural scientists is a genuine interest in how things work, plus either an innate or learned ability to "think" in a certain way that works for their field of study. I don't have a good way to describe it, but a sense that a chemist thinks like a chemist. A scientist is obsessed with learning how things work. The different fields are different approaches to finding that out, that work for their respective domains. Trying to figure out how a frog works by thinking like a physicist will result in a lot of dead frogs.
The other post mentions food science. Stodgy, yes. Fascinating, you bet. Food isn't going away. The problems of making food abundant, pure, healthy, safe, and ecological, are going to get more and more challenging. It can be stodgy because we have to control our impulse to try dangerous experiments on human subjects, or make a mistake that brings down a production line or triggers a recall. But oddly enough there are people who get their excitement out of working within that constrained environment.
You have to embrace the stodge. Something I've noticed about chemists, is that they tend to have the best discipline about running controlled, repeatable experiments. They keep the best notebooks.
Chemistry is closely related to materials science. Any realistic development of a material beyond the basic research phase will require the involvement of a chemist. Likewise drug manufacturing, etc.
(Brit here). A shop that sells pharma products is often colloquially called 'a chemists' but the people who work there are typically referred to as pharmacists. 'Chemistry' as studied at school and Uni is totally about chemicals in general and not drugs (which would be pharmacology). Someone who described themselves as a chemistry student or professor would almost inevitably be perceived as someone who is working with chemicals.
Myself, I'm about to finish my PhD in materials science and discover what I can really pivot to, wether related to materials/chemistry or using what I've learned about data analysis for any other area.
White noise has a flat spectrum from low to high frequencies. It has the standard noise sound that most of us recognize from digital systems.
Pink noise is shaped with a decreasing power as frequency increases. This results in more low frequency noise and less high frequency noise. This noise pattern occurs frequently in natural systems.
Different colors of noise have different sounds to our ears, of course. The color naming scheme is loosely intended to map to light spectral distributions. For example, blue noise has a rising power with increasing frequency, similar to how blue light has more energy at higher frequencies (shorter wavelengths).
Is there something ininformation or signal theory that benefits from creating truly random (or random but with some structure to it like blue noise not having lower frequencies) noise? As a chemist, I always model real-world noise with a gaussian distribution, so I don't really get where this could br used.