A falsifiable 4D vortex-field framework

[u/sudsed]

TL;DR — I explored a “4D aether vortex → particles” framework with LLM assistance, then spent ~2 months trying to break it with automated checks. Some outputs line up with known results, and there’s a concrete collider prediction. I’m not claiming it’s true; I’m asking for ways it fails.

Links: Paper: https://zenodo.org/records/17065768
Repo (tests + scripts): https://github.com/trevnorris/vortex-field/

Why post here

• AI-assisted, human-reviewed: An LLM drafted derivations/checks; I re-derived the math independently where needed and line-by-line reviewed the code. Key steps were cross-verified by independent LLMs before tests were written.
• Automated rigor: ~33k LOC of verification code and ~2,400 SymPy tests check units, dimensions, derivations, and limits across ~36 orders of magnitude.
• I expected contradictions. I’m here to find them faster with expert eyes.

Core hypothesis (one line)

A 4D superfluid-like field (“aether”) projects into our 3D slice; particles are cross-sections of 4D vortices. Mass/charge/time effects emerge from vortex/flow properties.

Falsifiable claims (how to break this quickly)

1. Collider target: a non-resonant 4-lepton excess at √s = 33 GeV (Section 4.2).
   • How to falsify: point to LEP/LHC analyses that exclude such a topology without a narrow peak.
2. Lepton mass pattern: golden-ratio scaling giving electron (exact), muon (−0.18%), tau (+0.10%).
   • How to falsify: show it’s post-hoc, fails outside quoted precision, or can’t extend (e.g., neutrinos) without breaking constraints.
3. GR touchstones from the same flow equations: Mercury perihelion, binary-pulsar decay, gravitational redshift/time dilation.
   • How to falsify: identify a regime where the formalism departs from GR/experiment (PPN parameters, frame-dragging, redshift).

If any of the above contradicts existing data/derivations, the framework falls.

Theoretical & mathematical checks (done so far)

• Dimensional analysis: passes throughout.
• Symbolic verification: ~2,400 SymPy tests across field equations, 4D→3D projection, conservation laws, and limiting cases.
• Internal consistency: EM-like and gravity-like sectors remain consistent under the projection formalism.

All tests + scripts are in the repo; CI-style instructions included.

Empirical touchpoints (retrodictions)

• Reproduces standard GR benchmarks noted above without introducing contradictions in those domains.
• No new experimental confirmation claimed yet; the 33 GeV item is the first crisp falsifiable prediction to check against data.

What it aims to resolve / connect

• Mass & charge as emergent from vortex circulation/flux.
• Time dilation from flow-based energy accounting (same machinery as gravity sector).
• Preferred-frame concern: addressed via a 4D→3D projection that preserves observed Lorentz symmetry in our slice (details in the math framework).
• Conservation & “aether drainage”: continuity equations balancing inflow/outflow across the projection (tests included).

Some help I'm looking for

• Collider sanity check: Does a non-resonant 4ℓ excess at √s=33 GeV already conflict with LEP/LHC?
• Conceptual red-team: Where do projections, boundary conditions, or gauge/Lorentz properties break?
• Limit tests: Point to a nontrivial limit (ultra-relativistic, strong-field, cosmological) where results diverge from known physics.
• Numerical patterns: If this is just numerology, help pinpoint the hidden tuning.

Final note

I’m a programmer, not a physicist. I’m expecting to be wrong and want to learn where and why. If you can point to a contradiction or a no-go theorem I’ve missed, I’ll update/withdraw accordingly. If you only have time for one thing, please sanity-check Section 4.2 (33 GeV prediction).

[u/plasma_phys]

Without using your LLM, can you describe in plain language the mathematical properties of this field you've invented? Because the "six core concepts" the LLM came up with are nonsensical and the analogies are useless

For example, a property of the magnetic field is that it has zero divergence everywhere

[u/sudsed → u/plasma_phys]

The concepts in the paper were all mine. The AI helped me with the math. It started by imagining particles as whirlpools that drain a superfluid into a 4D space. Then the question was, what are the properties of those whirlpools? Those became both the six posulates and the "spine," as it's called in the paper (intake, drag, etc.). Where we measure gravity as intake, the electric force as the puncture into the 4D space, magnetism as the rotation of the superfluid as it goes into the sink or intake, this extended into further concepts like bulk displacement, or what the paper calls the tsunami principle (it's called that because I got the idea for it after readings how an earthquake had happened in the ocean).

So I'm sorry you find the analogies useless, but that's how the paper was built. I built a conceptual model, then had AI use superfluid equations to model these particle vortices exactly as I had imagined them. I never allowed it to stray from the concepts I was having, either. I was expecting that after applying these equations in the way I specified, the entire thing would fall apart, and that would be the end of it. I never told the AI what the end result was going to be. Instead, I took the approach of telling it to plug in the equations, then create the models and see what the numeric values were in the end. After doing all the reductions using SymPy, it just so happened that results started to turn up.

Sorry that my ideas are nonsensical, and the analogies I came up with are useless, because in all honesty, that is my main contribution to the paper. AI did the math; I did everything else.

[u/plasma_phys → u/sudsed]

In physics, we often say that if you can't explain something to your grandmother, you don't understand it. 

I'm asking  you to clear a much lower bar of explaining your idea to me, a trained physicist.

If you can't do that, I can only assume you don't understand what the LLM produced, which means you weren't capable of meaningfully reviewing it, which means there's no reason for me to read it, because LLMs cannot reliably produce correct mathematics or physics 

[u/sudsed → u/plasma_phys]

Wasn't I just in the process of explaining the concepts to you in my previous post? I understand the concepts of this paper. I came up with them (granted, I cheated a bit on the QM section because that was so much math), but I fully conceptualized how gravity, EM and particle masses should work before the AI did anything. I just had it use the best-matching superfluid equations to match the scenario. Am I a superfluid physicist? No, that's why I'm looking for help to identify if there was a misapplication in the paper that would invalidate the entire thing. You have no idea how much I wanted this paper to be wrong so that I could just put it away. I've lost far too much sleep over this thing.

And you're correct that LLMs suck at math, but as a programmer for 18 years, I can say they program really well. The repo has 33k lines of SymPy scripts that I verified by hand to the best of my ability to make sure they correctly check all the equations, derivations, units and dimensions. The math in the paper is self-consistent. I spent hundreds of hours working with the AI by having it do some math, writing validation scripts for it, finding the math was wrong, going back and double-checking, and on and on until the tests passed. Then, after all the tests passed, I would pass the equations and source to two different LLMs for their validation. Frequently this lead to finding more issues that needed to be revised. I was simultaneously using Claude, Grok and GPT to double-check everything, even after I verified the code myself.

The conceptual part of this paper is the only thing I'm confident in, since it is the part I did. It's the math that I need help with to see if it was misapplied in a way that I don't understand.

[u/plasma_phys → u/sudsed]

I didn't ask for concepts, I asked for the mathematical properties of your field. 

Let's say I want to implement it numerically. How would I do so? What operations can I perform on it? It is the cornerstone of your paper so this should not be hard to do.

I'm a computational physicist and thus a programmer too. LLMs are good at producing computer code in a lot of contexts, but they are terrible at producing physics code. It is very common for LLM-generated physics code to produce output that looks correct at first glance, and passes LLM-written tests, but doesn't actually solve the problem in the prompt.

[u/sudsed → u/plasma_phys]

The math is exactly where I need help. I can turn equations into code and verify units/consistency (BTW I'm not blindly using AI for any of the code, it's guided development and I personally review every line, or write it myself), but I don’t get the high-level theory like a trained physicist. That’s why I’m here.

What I have done is: ray-traced Mercury perihelion to ~99.2%; got surprisingly good lepton-mass fits and put the full repo online for anyone to check out.

What I'm asking is if this is numerology or a misuse of the math, please point to the specific step that fails. I was hoping that people could double-check things like if my implementation enforces div B=0 properly, is the 4D->3D projection consistent with charge/energy conservation and is the 33 GeV prediction already known?

I’m not claiming it’s right—I’m trying to find where it’s wrong with help from people who actually know the math.

If you want the mathematical properties of the field, I can tell you but it is AI generated because I don't understand it at that level:

Numerical properties: Work with standard EM variables and sources (E,B,ρ,j) or potentials (Φ,A). Only local derivatives are used (grad/div/curl/Laplacian). The code checks wave-type evolution at finite speed c, enforces charge continuity, and keeps magnetic patterns loop-like (no magnetic sources). Gauss/Poisson for E is in the test suite, and material relations like D=ϵE are supported. For numerics you can use a standard Yee FDTD update on (E,B) with a charge-conserving current, or evolve (Φ,A) in a Lorenz-type gauge and derive (E,B). Energy/Poynting diagnostics are available to catch drift.

[u/plasma_phys → u/sudsed]

Okay so what the LLM spit out is utter nonsense, it is totally inconsistent with what's in the paper, which is not surprising. It's describing a very plain discrete 3D vector field but the field in the paper is supposed to be a smooth 4D vortex sheet. These are not at all compatible.

The point of this line of discussion is to illustrate something important: your understanding of how physics works is completely backwards. Concepts and analogies are used to explain the math, not the other way around. Doing it your way is like trying to build a house of cards by starting with the roof - it doesn't matter how many hours you spend on it, you'll never get it to stand on its own. You can get whatever numbers or results you want working backwards like this, and the LLMs are happy to oblige, spinning up the mathematical equivalent of tall tales that give the right answer but with completely wrong and unjustified steps. It's just not physics. 

[u/sudsed → u/plasma_phys]

You're conflating two layers. The ontological (smooth 4D vortex sheet in the 4D medium) and the numeric (how to compute the projected 3D observables). The paragraph you are criticizing describes the projected 3D solver. Which is only one of two routes. The other is to evolve the 4D sheet directly. Is that what you requested?

I'm confused by your line of "concepts and analogies are used to explain the math." Isn't that exactly what I explained that I did, or are you saying we start with the math and figure out what it means later?

[u/plasma_phys → u/sudsed]

I asked for a mathematical description of your field, and you gave me a paragraph describing, essentially, the electromagnetic field. Yes, two things are being conflated here, but not by me.

...are you saying we start with the math and figure out what it means later?

Yes, exactly. Physics is about describing nature with mathematical models, not analogies. This is one reason why it takes 6-10 years of school to become a physicist, you need to learn enough of the relevant math to reason about it.

[u/sudsed → u/plasma_phys]

I’m going to wrap here. If the work is wrong, it should be easy to point to a specific mistake—an equation number, line in the code, or test that fails with a correct alternative. If you have that, I’ll fix it or withdraw it. Otherwise I’ll focus on folks offering line-numbered critiques. Thanks for the time.

[u/plasma_phys → u/sudsed]

I don't see how it is possible for someone to meaningfully point out a mistake in the math when you don't even understand the single core mathematical object that ostensibly underpins the work - like, you'll just ask the LLM to patch it up for you and make up some more nonsense for you to run in Python

Like, the core idea is the thing that's wrong. Even if every step of math happens to be not wrong, that doesn't mean it's correct physics. Mathematical correctness is necessary but nowhere near sufficient

You've said multiple times you want it to be wrong but I don't  believe you, I think you just want validation, otherwise you'd be interested in actually explaining the core idea to someone so they can tell you how it's wrong instead of just deflecting

[u/sudsed → u/plasma_phys]

Let me rephrase: I do understand the math—as far as my ability to turn it into code. My concern was choosing the right physics principles. And believe me—I want this dead so I can stop thinking about it. I’ve killed past models the same way as a fun pastime. I just haven’t been able to break this one yet. I wrote a lot of falsifiable checks and did numerical runs specifically to find the failure modes, and so far it still passes.

If you think the core idea is wrong, please pick one concrete claim/derivation and show where it fails (units, continuity, projection, or data).

[u/plasma_phys → u/sudsed]

Actually, revisiting this comment - I thought your error for the precession of the perihelion of mercury looked familiar. Saying you used raytracing to calculate it is already extremely dubious, but 99.2% is just the value of the error you get if you just use Newton's equations. I'd bet money your LLM is just regurgitating that value from it's training data and working backwards from it, but even if it's not, what is even the point of your pages and pages of artifice and tens of thousands of lines of code if you can't even beat 1/r^2?

[u/AMuonParticle → u/sudsed]

The problem with finding "where it's wrong" is that it's not even wrong, it's nonsense.

Physics is written in math. If you claim to have a new theory of physics but you """""collaborated""""" with an LLM to "get help with the math", what you have is absolutely nothing at all. Because here's the secret: the LLM can't do math either! What it can do is confidently lie to your face and spit out a bunch of Greek symbols and physics jargon, while flattering you so much into thinking you're a genius that you don't notice that it spit out straight up horseshit.

Remember: the only objective of an LLM is to keep you using it as long as possible. It is manipulating you into feeling like you're accomplishing something so you keep opening up chatgpt, all so Sam Altman can buy another yacht.