‘A Swiss cheese-like material’ that can solve equations
penntoday.upenn.edu
penntoday.upenn.edu
http://www.ti.com/data-converters/adc-circuit/high-speed/rf-...
Also note the fast ones require 1+ watts per channel and cost $700+ each. Not cheap in power or in money.
https://arxiv.org/pdf/1804.08711.pdf
TWiML podcast of same: https://twimlai.com/twiml-talk-237-deep-learning-in-optics-w...
If you have any questions (about the specifics of the paper, or, more generally, about the process), feel free to send them over :)
---
[0] nqp.stanford.edu
In my experience trying to get RF measurements repeatable starts to become tricky at around 1% error. The dielectric constant of plastic parts changes constantly, capacitors age, etc. How sensitive is this device to temperature, humidity, barometric pressure, etc?
I'm talking about I guess simulators and some cheap kits to play around with photonics.
More classical optics setups with some decent lasers can be found off-the-shelf (though I'd have to look for consumer-type kits since the toys we have in the lab are a little more than my budget could personally handle ;). Either way, this is the best way to start since much of the subject really is based on doing experiments with light polarization, interferometry, etc., that forms the base of much of the work here (and many of the means of measurement). This is what this paper does, essentially, with the huge structures they've created (except with microwaves, which require some specialized equipment to measure).
Now, if you're interested in doing experimental work with photonic crystals, this question becomes a bit more difficult since it's essentially required that you have a foundry and some amount of cash to blow (as almost everything is fabricated and would require scanning electron microscopes and such to verify). You can also ship off parts to places like TSMC (which would likely form the basis of a somewhat expensive hobby), which I think deal with some small-scale manufacturing, but the time turnaround is pretty large as is the cost.
I have an idea for making microwave metamaterials, fairly cheaply, but don’t have the physics background to write the solver for the material structuring.
I have a heavy math background though, eg, dealing with convex optimization in the context of economics.
Overall, the mathematics itself is not difficult (essentially, everyone is using some simple preconditioned CG method for solving the linear problems, along with some [sometimes smart, sometimes not] meshing), but generating robust solvers is, almost universally, still an open problem. Depending on what you'd like to simulate and such, you're going to have to make use of the different properties of the operators you're working with to get really good results. We can email and I can say a little more given more details of your project/potentially guide you in a slightly better direction.
----
I’ve wondered when we will see FPGAs integrated on die with a regular CPU for a similar purpose.
Maybe by modelling dynamical systems as "neural nets" as in: https://arxiv.org/abs/1806.07366 and https://arxiv.org/abs/1808.08412
Or by using complicated physical systems we don't even understand to build Echo State Networks: http://www.scholarpedia.org/article/Echo_state_network
Wholly quantum - uses both wave and particle behaviours and entanglement
Wave computation like in article - just waves
Digital computation - just impulses or currents in conductors.Reminds me of the analog delay-line memories from the 1940s to the late 1960s (https://en.wikipedia.org/wiki/Delay_line_memory#Mercury_dela...) and bucket-brigade devices of the 1970s. (https://en.wikipedia.org/wiki/Bucket-brigade_device) But in a whole-new dimension.
Let's say I had that kernel stored in a database, is it just a matrix multiplication or two to calculate the solution? If so, doesn't that kind of invalidate the idea that this is a bottleneck problem that requires a speed-up? Especially once you take into account read/write speeds on the physical structure?
I guess my point here is the correct benchmark is pre-memoized code. I'd be interested how it performs against that benchmark.
So you can't iteratively improve your room, unless you don't mind fabricating all the kernels.
> “We could use the technology behind rewritable CDs to make new Swiss cheese patterns as they’re needed,”
Could be not much different from compiling and running stuff on an FPGA.
It’s not obvious how you can change the microstructure of a material to something you like in minutes.
How about 3D micro-structures? It’s hard enough to make a one off 3D structure reproducible, never mind a changeable one.
Well, there are 3D printers :)
This is about optical wavelength length scales -> feature sizes << 1500nm for infrared
3D printers’ feature sizes work for giga hertz waves (as a guestimate) assuming:
- The features can be printed - 3D have limitations after all.
- the materials that can be 3D printed are optically suitable.
Computing a kernel is an inverse problem, which is more difficult than solving a system for a given right-hand-side.
Linear algebra tells us to never invert a matrix, and here we're actually fabricating the inverse physically.
Or, of course, perhaps I'm misunderstanding something.
Then you build it. More time.
Then you run it for thousands of different inputs. There’s the benefit.
For example:
You build a kernel describing the acoustics of a sound (ie a body of water) once.
Then you can solve instantly what and where sounds (acoustics) are coming from
These are basically equations g(t) = Integral_t K(t,s) * f(s),
where K(t,s) (the kernel) and g(t) are known, but f(s) isn't. In physical applications, t is usually time. A solution f(s) could be fed into the input of the next device, at least mathematically.
I know that until digital computers came along, structural analysis was done with strains lacquer techniques, so the circuit way seems limited
This article delivered. Such a cool experiment and field of study.
Piquing your interest with the title is just good headline-writing. It only becomes clickbait when the title is a cynical perversion of the content.
If a title is interesting enough to get lots of people to click on it, it is by definition clickbait.
Titles for interesting articles should themselves be interesting, as boring titles for interesting articles would increase one's chance of missing them.
An interesting title is only objectionable is the article itself is not worth reading.
So people fixate on the title and call any interesting, well-written title "clickbait" regardless of whether the article itself is worth reading or not.
I'm simply pointing out that such an enticing title is not necessarily bad if the article itself is worth reading.
The alternative is a boring title, which is a disservice to any article worth reading.
That's supposed to just be called journalism.
I thought I'd be opening an article about the human brain. Pleasantly, it's about an attenuation-based analog computer that for some reason requires metamaterials. The most novel aspect, for me, is the computational method used to design and direct the fabrication of the apparatus.