Safe to say NASA has much higher standards of quality they must meet. When you're designing something that cannot break for months or years.. after riding on a rocket.

I was about to say the article does mention it, but I went back and reread to confirm and realized I had used my own knowledge to supplement or something because yeah, they literally don't talk about the cons of this tech in the article at all. That's bad. I guess I understand why after looking at the type of the content the site produces, but it's still disingenuous when the crux of this problem right now is figuring out whether these designs will hold up at the microscopic/atomic level under the extreme temperature and pressure forces, states, and changes they'll be routinely subject to. Besides sending them up to test, there isn't currently a nice way to ensure the performance specs.

I think the tech itself is great. I'm also really fond of the innovations they've made with sound waves at Fabrisonic--for the longest time it seemed like magic because I couldn't wrap my mind around weaving sound waves into physically-real metals. After reading a couple research papers and going through the math/physics, I finally have a handle on it and how clever it is. It reminds me of the foundations of string theory. Both generative-design techs will undoubtedly lead to innovations and spin-off techs in other industries; biomedical being a primary branch.

I'm a biophysicist by title, but I have graduate degrees in astro/computational physics as well so this is definitely something I've been keen on. I'd love to ultimately get to work with the tech via collaboration or get something in my lab for student research if that's ever a possibility. It's a really cool next step to take that I think is brilliant. We already hijack so many natural processes in the lab (gene copying/tagging, medicines, plasmid-insertions etc.), that it makes sense to use a more biological process in the scaffolding of aerospace and rocket engineering as well.

The cons are extra important to pinpoint. Especially to the engineers, researchers, and scientists who are particularly interested in generative design. Finding ways to solve those problems are the very reasons some people even exist in these industries at all. I'm actually really impressed at the time scales they have this operating at. I'd be interested in seeing exactly what changes it comes up with on average in 2-4 hours period. That's an insane turnaround time. I was expecting changes on the level of days or weeks. Thus, I'm also curious about how the system is evolving and analyzing each generation. At this point I'm just rambling though, so I'll leave it at that. I agree, they should have outlined the key issues alongside the benefits.

There is a big terminology issue going on here.

A GA is fundamentally different from AI because a GA does a very specific thing: It evaluates a set of solutions (called a population) and uses some method to choose some of those to reproduce (selection) and then recombines some of them (crossover), and applies random changes (mutation) to generate the next population, and iterates hopefully increasing fitness over time. It is an algorithm for optimizing solutions, and is not specific to things like learning systems or neural nets.

GANs are neural networks trained in a specific process, where there are networks that are solving the problem and networks that are trying to generate difficult input, to put it simply.

Reinforcement learning is a broad learning approach that covers a ton of different learning algorithms all with their own secret sauce, and it can be applied to decision making agents of many kinds, including neural nets and AI systems, but also other things like simple robots with state machines.

It would be incredibly disingenuous to say GAs are AI/ML, equivalent to GANs or a kind of reinforcement learning because those things are all very different and specific in ways that they aren't compatible ideas.

For example, some GA researchers use GAs to generate patches to buggy code. This has nothing to do with learning, there is never a model of the program, the evolved solution is purely a patch description of code. It bears no resemblance to these other methods and has nothing to do with neural nets/ai/etc. It makes no sense to try to lump these things together when some are concepts, some are algorithms, some are specific neural network designs, all with different components, purposes, and applications.

Now they can be used in conjunction. Like if you have ever heard of NEAT, it is a GA for evolving neural networks, and the neural networks are AI/ML. Also you can evolve an agent for a reinforcement learning process, but they would be separate steps. Neither is a subset of the other.

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Acronyms, initialisms, abbreviations, contractions, and other phrases which expand to something larger, that I've seen in this thread:

|Fewer Letters|More Letters| |-------|---------|---| |CNC|Computerized Numerical Control, for precise machining or measuring| |DMLS|Selective Laser Melting additive manufacture, also Direct Metal Laser Sintering| |QA|Quality Assurance/Assessment|

^(3 acronyms in this thread; )^(the most compressed thread commented on today)^( has 8 acronyms.)
^([Thread #8570 for this sub, first seen 15th Feb 2023, 21:06]) ^[FAQ] ^([Full list]) ^[Contact] ^([Source code])

Good question; not really sure how their QA process works but they probably have some type of intrusive scanning

And what the hell is a "Roman Telescope"?

<\sarcasm>

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Wouldn’t it be terrible if they named something so scientific after somebody so anti science… lol the Nancy Grace telescope on Fox News …

They now think the dark energy is vacuum energy in older black holes .

Very interesting article that was posted not long ago on here.

https://phys.org/news/2023-02-scientists-evidence-black-holes-source.html

How is a genetic algorithm that optimizes for a set of constraints fundamentally different from a GAN or reinforcement learning model except in implementation details and resource-efficiency?
The discriminative network in a GAN is the provider of constraints aka part of the training dataset or the measurer of fitness.
The generative network proposes solutions and refines it's weights based on the fitness of the output.

There are differences but the premise is more similar than dissimilar.

Your funding would also likely be better if you could convince people that it is a form of AI maybe branded as a subcategory of supervised reinforcement learning.

Where is the intelligence in the glorified inverted indexes with result blending bolted to them that are paraded about these days?
Inventing authors and papers that sound plausible when asked for citations is a strong indication that the smoke and mirrors make people ascribe a lot of intelligence that is simply not there.

For anyone else as unaware as myself, it's named after the astronomer Nancy Grace Roman, not the horrible TV personality Nancy Grace.

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Quality can not be made by testing, it can only be achieved by working on the process.

• some japanese dude probably

This is Reddit, home of Dunning–Kruger

kind of like europe with ariane 6, traying to compete in price reductions but trailing in rehusability

tbh i'd love to see somebody one day to leapfrog up from what we have with newer technologies towards single stage planes, hypersonic engines, and maybe in the future if it pans out plasma jet engines

one can deam, can i?

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Yes, and it really worked! I was over the moon when I found that out!

The Company Phase3D is doing something similiar but only with a heigthmap of the spreaded and/or melted powder, at the company im working in we're currently in the planning stage to build somehting like this in our Ceramic 3D Printer

Maybe because most of the things wrote in an article anywhere is mostly for making advertisement money and not for informing anyone.

This type of rapid survey will also enable other space telescopes to use their time more efficiently, or better rank existing applications for extremely valuable Hubble and JWST time.

What's the difference in QC testing these parts vs. QC testing literally any other Aerospace part? When I worked in GA, the people doing welding needed to have their welds all x-rayed, magnafluxed, or whatever. Certainly there are ways of finding and addressing porosity or voids in a 3d printed part.

In short, it functions like a DMLS printer where you have a build plate that descends and a moving blade to deposite the layer. The idea is figuring out a way to completely decouple the build plate after a layer is deposited so that it can be weighed, the area of the layer is known, and then the build plate is returned to its original position. The challenge is the tolerances involved. We're talking fractions of a milligram, and the build plate needs to be returned to the same position with thousands of an inch (mixing units, fight me). The other challenge was not losing a single bit of powder during the decoupling and not disturbing the layer. Then you also had to purge the seal in a pure nitrogen and dry environment.

I left the project fairly early though as I had changed jobs. But the prototype was showing promise although never fully tested.

I think you are missing my point entirely which is merely that we deserve to know more.

That's it. Everything else is just me speculating as an example and your own assumptions about ME based on those speculations while ignoring the only thing I cared about.