As somebody who almost fried his computer during the antenna design course to optimize a dipoles array with a (not optimized) genetic algorithm, I really like this content.
These designs fascinate people who haven't designed antennas. I don't doubt that throwing enough computational power at optimizing antennas will produce antennas optimized for something at the expense of something else but if you're a casual what you should notice is that these papers never mention the "something elses". You can get a paper out of just about any antenna design, btw. There's also a type of ham that will tune up a bedframe or whatever. So just getting something to radiate should not be confused with advancing the state of the art.
These antennas found their way into the utterly savage "pathological antennas" chapter of Hansen and Collin's _Small Antenna Handbook_. See "random segment antennas". Hansen and Collin is the book to have on your shelf if you're doing any small antennas commercially and that chapter is the chapter to go to when you're asked "why don't you just".
A long time dream of rocket scientists is single-stage-to-orbit. Ideally you'd have a vehicle that takes off and lands like a conventional jet plane at a regular airport. I've always thought that perhaps AI and evolutionary algorithms might be able to navigate a way through the various tradeoffs and design constraints that have stopped us so far.
Very cool. Evolutionary Algorithms have kinda been out of the mainstream for a long time. They are good when you can do a lot of "black-box" function evaluations but kinda suck when your computational budget is limited. I wonder if coupling them with ML techniques could bring them back.
> I wonder if coupling them with ML techniques could bring them back.
EAs are effectively ML techniques. It's all a game of search.
The biggest problem I have seen with these algorithms is that they are wildly irrespective of the underlying hardware that they will inevitably run on top of. Koza, et. al., were effectively playing around in abstraction Narnia when you consider how impractical their designs were (are) to execute on hardware.
An L1-resident hill climber running on a single Zen4+ thread would absolutely smoke every single technique from the 90s combined, simply because it can explore so much more of the search space per unit time. A small tweak to this actually shows up on human timescales and so you can make meaningful iterations. Being made to wait days/weeks each time you want to see how your idea plays out will quickly curtail the space of ideas.
As somebody who almost fried his computer during the antenna design course to optimize a dipoles array with a (not optimized) genetic algorithm, I really like this content.
These designs fascinate people who haven't designed antennas. I don't doubt that throwing enough computational power at optimizing antennas will produce antennas optimized for something at the expense of something else but if you're a casual what you should notice is that these papers never mention the "something elses". You can get a paper out of just about any antenna design, btw. There's also a type of ham that will tune up a bedframe or whatever. So just getting something to radiate should not be confused with advancing the state of the art.
These antennas found their way into the utterly savage "pathological antennas" chapter of Hansen and Collin's _Small Antenna Handbook_. See "random segment antennas". Hansen and Collin is the book to have on your shelf if you're doing any small antennas commercially and that chapter is the chapter to go to when you're asked "why don't you just".
A long time dream of rocket scientists is single-stage-to-orbit. Ideally you'd have a vehicle that takes off and lands like a conventional jet plane at a regular airport. I've always thought that perhaps AI and evolutionary algorithms might be able to navigate a way through the various tradeoffs and design constraints that have stopped us so far.
As a rocket scientist I assure you it's been tried
Very cool. Evolutionary Algorithms have kinda been out of the mainstream for a long time. They are good when you can do a lot of "black-box" function evaluations but kinda suck when your computational budget is limited. I wonder if coupling them with ML techniques could bring them back.
> I wonder if coupling them with ML techniques could bring them back.
EAs are effectively ML techniques. It's all a game of search.
The biggest problem I have seen with these algorithms is that they are wildly irrespective of the underlying hardware that they will inevitably run on top of. Koza, et. al., were effectively playing around in abstraction Narnia when you consider how impractical their designs were (are) to execute on hardware.
An L1-resident hill climber running on a single Zen4+ thread would absolutely smoke every single technique from the 90s combined, simply because it can explore so much more of the search space per unit time. A small tweak to this actually shows up on human timescales and so you can make meaningful iterations. Being made to wait days/weeks each time you want to see how your idea plays out will quickly curtail the space of ideas.
This has to have been done in more modern times in simulation of the EM field for a better design instead of practically