MITSardine

Basically you don't need to think much about the computer itself, unlike C or C++. Just pass in your arrays or pointers (everything is passed by reference anyways), crunch numbers, and it'll be about as optimized as carefully __restrict__ laden C code.

There's also fewer traps to fall in than C++ which is elephant sized in scope to Fortran's mouse.

> Normal finite elemts does not care. The mesh ist just put on your 3d body that can be an stl or a step or whatever. The FEM only knows the nodes and nothing else. The mesh geometry is completely separate from the CAD file.

I have to disagree somewhat. Engineers rarely run a single simulation, rather they do convergence studies. For some problems, it might be fine to refine while keeping the same geometry (since you have no arbitrarily accurate continuous representation), but not all.

So, yes, you can run toy simulations on any mesh, but I don't think any serious shop will consider a workflow where CAD is completely inaccessible.

There are some ways around it but the cure might be worse than (or rather just as sophisticated as) the poison. Like extrapolating what the geometry might be with high-order elements or fitting NURBs.

You can find NURBs fitting in decades old textbooks (such as "The NURBs book"), but you have to be aware that this is an approximation problem as the only exact fitting is the one where the output is the input.

Also prone to robustness and quality issues. You're better off outputing a BREP directly.

Exactly, and those answers are why it’s a hurdle and a leap of faith.

The company has to adhere to labor laws in foreign countries (most likely plural if this is a general policy). That’s quite the overhead and opens you up to legal trouble.

The employee has to trust their government can prosecute or otherwise coerce a foreign company with no entity on their territory (otherwise they’d just get hired normally through the subsidiary).

LA and NJ remain two states of a common country and I'm sure that simplifies a lot of things.

Although none of this applies if the company is a multinational with offices in the employee’s country. But then that is offshoring rather than remote work.

It takes a lot of trust (on both sides) and complexity to establish a working relationship across two jurisdictions, if at all possible.

For instance, what labor laws apply? And who enforces them? And how, given parties in two different countries?

Does the company want to subject itself to some arbitrary country’s tax man or labor law enforcer prosecutions? Does the employee want to work under effectively zero labor protection? Possibly, but rarely.

It’s not even always possible, it’s very complicated to work remotely in France, for instance. The state cracks down on one person one client companies, and EoRs beyond being impractical (as they must adhere to drastic labor laws compared to the US) are in a legal gray zone.

Fascinating work and write-up. This is like a vignette of France: weaponized mauvaise-foi, sacred institutions and, more generally, some remnants of an aristocratic hierarchy, and also probably a lot of honest technical incompetence.

Open book need not be easier, it entirely depends on the exam.

This was too long ago for me to remember the details of but, just making an example here, we might have had a course on homogeneization of elliptic problems using a model PDE with a div(A grad u) term throughout, and then the exam was about a PDE with a curl(A curl u) and some other added terms.

Notes carry the method and theoretical tools but you then still need to apply them to a new problem.

So, if anything, our open book exams were harder and a better test of understanding and mastery. You couldn’t rote your way into a good grade.

It's just labor law, these are countries where job security is considered very important. As a result, a lot hinges on having an indefinite duration contract.

The same rule exists in France and you'll have a hard time getting a loan or even a rental if you don't have the sacrosanct CDI (Indefinite Duration Contract), or legal guarantors with one.

It may seem absurd in these edge cases, but it's a way of putting pressure on employers to either hire people for the long term, or let them go.

In this case, it's not necessarily a bad thing in 90% of cases either (because those that don't get permanent employment despite scoring ERCs are certainly very rare), it prevents people from being stringed along in a lab where they're not really building up a career, but rather setting themselves up to be 40 and unemployable, when they're eventually not given the job security the lab would have given 5+ years earlier if they ever intended to/could do so.

You presumably spent 15 or so years in school to learn things of which many only have very indirect applications, and at most a small minority directly applied to your career.

Should we similarly get rid of mandatory education? Most of it is useless after all.

From the article, it sounds more like these funds are the research budget (what you pay other people with). This is quite attractive, students are typically < 200k / PhD, so you can fund quite a few theses with this right off the bat. Basically max out the lab for the grant duration.

I don't know what their salaries would be exactly. This is probably most dependent on where they land, as salaries are very often standardized in Europe. There's usually salary grids per institution dependent on seniority with some milestones being merit-based. Quick google search indicates gross salaries for Professor level (mid/late career) researchers to be around 110-165k€ in NL.

That seems pretty sweet. It's comparable to what US professors make in the hard sciences, as far as I know, with lower CoL than most areas where professors make similar salaries.

And again, salary isn't everything to a researcher. If they can't hire, they're pretty strapped. At this career stage, they're managers, not so much individual contributors. I'd say a maxed out lab for 5 years off the bat is pretty enticing, which also gives time to get up to speed on European funding schemes like ERC grants.

I was a postdoc in the US during Trump's reelection and there were several months where my institution and others had completely cut off scientific staff (such as postdocs, research scientists and engineers) recruitment due to NSF defunding and other threats. Even now, they got taxed on endowment and lost basically 10% budget. This is considerable, and a source of stress for researchers and their current/prospective staff. You can't work properly if you're under the Damocles sword of being laid off / having to lay off your staff.

Stuff just gets done, I guess? Projects move faster, people onboard faster with less intervention, etc. The speedup seems noticeable enough that it doesn’t need precise measuring.

As a disclaimer I haven't read the article, nor do I know much about simulating instruments in particular, but I just wanted to point out that accurately simulating the physics of a musical instrument is most likely still a very difficult problem.

I have no doubt there's been analytical/semi-analytical models around for decades. I mean a program that can take an arbitrary geometry or class thereof with specific materials and simulate the high frequency vibrations and model interactions with the body with high fidelity (not through ad-hoc models) is probably still out of scope of real time simulation.

My point is really that there's often families of models that deal with one thing, from semi-analytical first coded in Fortran in the 80s that can run in milliseconds but is only valid in certain configurations with a low degree of accuracy, to "first principles" simulations that may well require a supercomputer to produce results to a useful degree of accuracy (and not in real time). So, just because you see someone claim they can "simulate X", and then another makes the same claim 40 years later, that doesn't mean they're doing the same thing.

For instance, aeronautics has XFOIL. It's a semi-analytical model first devised in the 80s that computes aeronautics coefficients for a certain class of airfoils (NACA). My understanding is it's a very clever, and industrially significant, piece of code, but ultimately it works in a narrow regime with some heavy simplifications. You can now get results from this in real time on a webpage. A proper CFD calculation to a NACA wing will take in the order of minutes to hours on a workstation (depending on requested precision and settings, e.g. speed of air), and while closer to first principles, it's still using physical simplifications (RANS). So yeah, although nominally people have been "simulating airfoils" for 40 years, the techniques have refined considerably, and will continue to do so (practical LES and, someday, DNS). It might be another century that people are still "simulating airfoils" in ever more accurate (nailing down within the constraints), high fidelity (lifting constraints) and generic ways.

Back to instruments, this is a difficult coupled problem, in fairly high frequencies (high frequencies = more expensive), with possible fluid-structure interactions, not to mention the geometries are fairly complex (to even get a workable mesh to begin with). My uneducated guess is we're still at either semi-analytical, or at the "considerably simplified first principles" stage for this type of problems. Just like DNS, I'm sure you could "just resolve the scales and run it through a simulation with a really tiny time step", and this is liable to be similarly expensive as DNS (million dollar single simulation). Additionally, they have to deal with the human ear, which is perhaps more unforgiving than an error plot on drag or lift. So I wouldn't dismiss news of instrument simulation as stale just because someone made something that produced similar artifacts in the past, as the methods will continue to evolve considerably.

Great article, thanks for sharing.

I didn't know (but should have assumed) AI-generated podcasts existed. That's depressing.

I imagined if mankind had the ideal machine, that could automate anything, we would get rid of dull office work and back breaking physical labor, but not the things that are actually enjoyable: sharing with each other, entertaining each other, making art. I imagined a lively world of live performance and creation, since all subsistence work had been taken care of. Instead we might end up in the world of fifteen million merits.

It seems people don't mind letting their minds be hacked by machines that can create the form of what they find enjoyable, if not the substance. But I guess there's always been slop and the public for it. To imagine actual people wasting their limited time on Earth listening to these GPT logorrhea podcasts is truly depressing. The unchemical soma.

What are we even supposed to spend our days doing in this bright future of the AI champions'? Stop automating away the things that give people purpose, tackle real problems instead.

To take this further, don't LLMs justify lowering the "barrier to attention"; i.e., if it only takes Claude's and not the hacker's eyeballs on the software, won't people find vulnerabilities in custom software for one too?

Besides that, one could easily imagine software created for similar purposes ("make me a file editor") by the same tool or handful thereof (claude and a very small "etc" for completeness) might share similar vulnerabilities, so this kind of broad net might be even cheaper to cast than one might imagine at first.

What are these languages? I'm curious because C++ is the standard in my community so it's all I ever hear about (and Rust from the adventurous few).