Here is another summary of the situation actualized by the OpenAI 57 page symbolic/analytical proof of blow-up of solutions to the unforced Euler equations:
måndag 5 oktober 2026
Euler's Equations: Symbolic vs Numerical Mathematics
söndag 4 oktober 2026
Per Enflo 1944 - 2026 Mathematician and Pianist
Per Enflo on September 28 in the middle of the next step on the daily walk with his wife Lena in Östervåla Uppland, under inspection of plants on the ground in the spirit of Linné and with inspiration from the sky finally closing his constructive proof of the Invariant Subspace Problem for Hilbert Spaces, took a last breath sending a shock wave to family, friends and mathematical community. Per Enflo is certainly the most famous Swedish mathematician all times by having solved named major problems, also concert pianist expressing the true meaning of the music of Mozart, Beethoven, Schubert and Chopin.
I met first Per during my post doc years 1974-76 at the math department of the University of Chicago, when Per visited as the new shining star of Functional Analysis, with offers from all the big universities, after having solved one of the key open problems in that area formulated by its founder Stefan Banach in the 1930s, as a 9 page Counterexample to the Approximation Problem in Banach Spaces published in Acta Math in 1973, which took the math community with storm. Watch the documentary movie about Banach with Per in the main role (and me with little side role) and Per's home page.
Per then followed up in 1981 with an 101 page counterexample to the Invariant Subspace Problem in Banach Spaces, published in Acta Math after 6 years of refereeing, to return 40 years later to the case of Hilbert spaces with an explicit construction of an invariant subspace.
It took 43 years before we met again, in Stockholm in 2008 when Per had rejoined with his love from youth Lena and returned to Sweden after 25 years in the US, and Per welcomed me and my wife Ingrid to his piano trio concert at the Mazer Musical Society. We found each other on the spot into a 20 year long friendship along with our wifes, with music, math and love. It is very sad that Per with his very kind person and amazing talent is no longer here. In the Swedish math community we shared experiences of exclusivity as a special bond.
Per was a master of constructive mathematics, constructing an invariant subspace for any continuous linear operator T in Hilbert space H, by constructing a sequence of vectors converging to a vector for which repeated application of T does not span H and thus forms an invariant subspace. Per was also a master of constructive music as combination of body/hand and soul/mind with ability to on the spot transpose any given sheet music to any key. We could meet in hands-on constructive math/piano but also lofty speculation, never any argument.
On Sept 8 and 26 another shock wave into the math community had been sent by OpenAI as constructive proofs of blow-up of solutions to the equations of Navier-Stokes (167 pages) and Euler (57 pages) performed by AI.
Per expressed that he had been lucky to not meet this seemingly formidable competition before, with AI able to construct proofs of any number of pages, beyond understanding by human mathematicians. Per could thus pass on to a heaven of math without shattered beliefs with his usual happy "that is also ok" and with his Musical Legacy completed in 15 CD on Spotify.
Pictures from Aug 20 2026:
OpenAI vs Clay Navier-Stokes vs Numerical Analysis
Here are two new articles connecting to the recent hype of AI doing mathematics instead of mathematicians:
- Does AI pull the carpet under mathematics education?
- What is the meaning of AI proofs of mathematical theorems?
- Is the main role of a mathematician to prove theorems?
- What is the role of numerical analysis?
tisdag 22 september 2026
No Longer Any Mission for Theoretical Physicists?
Swedish theoretical physics (string theory) Ulf Danielson today in Swedish mainstream media (SvD) concludes that with now AI writing articles and then including whatever theoretical physics is needed, there is no longer any role for theoretical physicists like himself with former prime role to contribute that element to science and education:
- Research is becoming meaningless.
lördag 19 september 2026
Smoothness vs Wellposedness vs Clay Problem vs Ghost Solutions
The official Clay Navier-Stokes Prize Problem asks about existence of smooth solutions but does not mention uniqueness or wellposedness as continuous dependence of the solution on data. This is because in the standard mathematical analysis of differential equations, uniqueness/wellposedness is viewed to be a byproduct of proving smoothness through bounds of derivatives of the solution in terms of data.
Asking for smooth solutions in the Clay Problem thus is viewed to include uniqueness without specific mention. But this opens an ambiguity by allowing the continuous dependence to include Lipschtiz or continuity constants of any size. This opens to viewing a turbulent solution with very large derivatives as a smooth solution with very large Lipschitz constants effectively eroding the very meaning of continuous dependence.
The true nature of turbulent flow is non-smooth with continuous dependence of mean values but not point values, thus exhibiting a form of weak wellposedness allowing meanvalues such as drag and lift to be computed as well determined quantities with continuous dependence on data.
The official formulation by Fefferman as leading mathematical analyst thus reflects a form of mathematical analysis, which misses the main character of the Navier-Stokes equations with small vanishing viscosity as having turbulent solutions, which are non-smooth without singularities, thus falling outside the Fefferman formulation.
Fefferman's mistake was to set viscosity to unity for a problem with small/vanishing viscosity as essence. In a math test that would give an F enforced by the fact that the Euler equation with zero viscosity is mentioned in the same breath as Navier-Stokes. The Clay problem debacle shows the effect of separating math from physics in a problem with physics origin.
The Clay problem thus needs a reformulation bringing in the turbulence very clearly expressed as motivation, but then forgotten. Without reformulation the problem will continue to direct major efforts into capturing solutions of no interest, with the recent OpenAI solution now shown by Constantin et al to be a ghost solution.
fredag 18 september 2026
Clay Math Institute Response to Criticism of Navier Stokes Problem Formulation
Response from Martin Briden President Clay Mathematics Institute:
My response:
Letter to Clay Math Institute on Navier-Stokes Problem
I have today sent the following letter to Clay Mathematics Institute including this analysis of the OpenAI solution. Copies to Terence Tao, Peter Constantin and Charles Fefferman (formulated the problem).
President
Clay Mathematics Institute
PresidentClay Mathematics InstituteI want to convey the information that the formulation of the Clay Navier-Stokes problem is incorrect both mathematically and physically, because the fundamental aspects of (i) wellposedness and (ii) turbulence, are not included, as exposed in detail in the following sequence of blog posts:The result is that the problem cannot be given a meaningful solution and thus does not serve well as a Prize problem. Evidence is given by the fact that no progress towards a solution has been made.I have tried to engage Charles Fefferman, who has formulated the problem, Peter Constantin, who acts as a referee, and Terence Tao, who is working on the problem, into a discussion, but I get no response.I hope this way to stimulate discussion, which I think would be more constructive than no discussion.SincerelyClaes Johnsonprof of applied mathematicsRoyal Institute of Technology, Stockholm
torsdag 17 september 2026
Meaningless OpenAI Solution to Clay Navier-Stokes Problem
Here is a post composed by Claude from my prompts about the Clay Navier-Stokes Millennium Problem.
A Meaningless Solution to a Meaningless Problem
The Clay Navier–Stokes Problem asks whether solutions of the incompressible Navier–Stokes equations stay smooth, or blow up in finite time. Recently a machine-generated proof has been offered which constructs, at every positive viscosity, a forced flow whose velocity becomes unbounded.
There is a simple test any such construction has to pass, and the official formulation never asks for it: report the constants as functions of the viscosity ν. Let us apply it.
One solution, rescaled
Navier–Stokes rescales. If v solves the equations at viscosity one with forcing g, then
u(x,t) = a·v(bx, ct), a = νb, c = ab, f = a²b·g
solves them at viscosity ν. The map is onto. A single unit-viscosity blow-up generates the entire family. So a theorem asserting blow-up "for every ν > 0" is not a family of results. It is one result, photographed at different magnifications, and it contains exactly what its ν = 1 member contains.
All the content is in what the rescaling does to the constants, and there are only a few ways to spend the freedom:
- Hold the solution at size one. Then the forcing amplitude is ν−1: the force required diverges as the viscosity falls, and leaves any fixed admissible class — including the one Fefferman specifies, which fixes its constants once and for all.
- Hold the forcing fixed. Then the velocity amplitude is ν1/3 and the active length ν2/3: both vanish.
- Hold the blow-up time at t = 1 — the natural normalisation. Then everything vanishes together:
amplitude ν1/2, length ν1/2, forcing ν1/2, gradient 1
At ν = 10−8 the flow reaches an amplitude of 10−4 on a scale of 10−4 under a force of 10−4, and then becomes unbounded. The family converges to the zero solution driven by zero force.
The limits do not commute
Write the family out. With blow-up at t = 1,
|uν(t)| ~ ( ν / (1−t) )1/2
and the two factors pull against each other. Let t → 1 first, at fixed ν: infinity. Let ν → 0 first, at fixed t < 1: the zero solution. Infinity one way, nothing the other. The joint limit has no value at all; it depends on the path taken.
The critical path is 1 − t ~ ν, where the amplitude is of order one. And there, the local Reynolds number is
Re = |u|·ℓ / ν = ν1/2(1−t)−1/2 · ν1/2(1−t)1/2 / ν = 1
identically — independent of both t and ν. The singularity sits permanently at the viscous scale. It does not pass through the inertial range on its way to blowing up; it never enters it. Turbulence is the statement that Re ≫ 1 over a wide range of scales. This object is at Re = 1 at every instant of its life.
It is a shock, with a point's extent
What is this thing? An O(1) velocity change across a thickness ν with gradient ν−1 is the viscous shock profile. For Burgers, a jump U relaxes over δ = ν/U, and its dissipation per unit area is U³ — independent of ν. That is the classical anomalous dissipation, the one-dimensional model of the very mechanism Onsager's conjecture concerns.
So the construction has the profile of a shock. What it does not have is a shock's extent. A shock is a surface: thickness ν in one direction, order one in the other two, hence volume ν and dissipation of order one. This object is ν in all three directions — volume ν³, dissipation ν², carried on an energy ν³. Both vanish.
The same local profile, spread over a surface, dissipates at a rate independent of viscosity. Concentrated at a point, it dissipates nothing. The construction is made to blow up by being denied the dimensionality that lets a shock carry energy.
And it is not even a counterexample
Here is the sharpest point. On the critical path, what diverges is the gradient:
|∇u| = |u| / ℓ = 1/(1−t) → ∞, while |u| ~ 1 stays bounded
— and note that the gradient does not involve ν at all: at every viscosity the same gradient is reached at the same time-to-blow-up, which is the sharpest possible statement that the family is one solution rescaled.
But Fefferman's alternatives ask for the velocity to become unbounded. It does not. At 1 − t ~ ν the family is a bounded flow with diverging gradients, which is neither horn of the dichotomy. It is the third category — non-smooth and non-singular — the one the formulation has no name for, and the one in which every flow of physical interest actually lives. The velocity becomes unbounded only afterwards, in a window of duration of order ν that shrinks to nothing.
A meaningless solution to a meaningless problem
The Clay problem is announced as a problem of turbulence — the wake behind the boat, the air behind the aircraft — and then formulated as a problem without it, asking only whether |u| stays finite. Between the description and the statement, the subject has been sorted out.
Put the turbulence back, in the only form analysis can digest — a turbulent viscosity depending on the local velocity gradients — and Fefferman's alternative (A) ceases to be open at all. Global existence and uniqueness follow by standard Sobolev-space techniques, as Lions showed in 1969, for a stress augmented by |∇u|1/2, with a coefficient as small as one likes. And at p = 3 that added term is the Smagorinsky model. The regularisation that makes the problem tractable is a turbulence model. The formulation excludes turbulence from its alternatives, and the price of that exclusion is precisely the term whose absence makes it hard.
So we have a construction which, taken to the limit where fluids actually live, converges to the zero solution driven by zero force; which lives at Re = 1 throughout; which is a shock deprived of its surface; and which, in the regime where it has an O(1) amplitude, is not a counterexample to the stated alternative but an instance of the category the formulation declines to name.
A meaningless solution, then. But the deeper trouble is that it answers exactly what was asked. The dichotomy smooth or singular is empty because the physical solution is neither: turbulent flow has bounded velocity and gradients growing without limit — Hölder continuous with exponent 1/3, the finest structures of size ν3/4 across which the velocity varies by ν1/4. Not smooth, so regularity theory cannot reach it. Not singular, so no blow-up theorem describes it.
What should be asked instead is wellposedness: which outputs of this flow are stable, and to what tolerance. Drag, lift and mean pressure are stable and computable; the pointwise velocity is neither, and no theorem asserting that its trajectory is unique will make it so. Uniqueness in a function space and stability of an output are different properties, and only the second is what a computation, an experiment, or an aircraft depends on.
The prize was offered for the wrong property. It should not surprise us that what claims it is a solution to nothing in particular.
söndag 13 september 2026
Mathematics Education vs AI vs BodyandSoul vs John Dewey
Recent posts on the OpenAI solution to the Clay Navier-Stokes Millennium Problem reflect on the monumental challenge to professional mathematicians presented by a AI agents showing to be able to prove mathematical theorems.
The challenge also strikes mathematics education and here (or below)is what chatGPT delivers on prompt asking about the relevance of John Dewey in the new landscape of AI.
The BodyandSoul project also gets new actuality with AI.
ChatGPT:
John Dewey (1859–1952) fits surprisingly well into the AI era—but AI also exposes a weakness in the way Dewey’s educational ideas have sometimes been interpreted.
Dewey’s central idea was that education should not primarily consist of transferring a fixed body of knowledge from teacher to pupil. Learning occurs through inquiry, experience, problem solving and reflection—often summarized, somewhat simplistically, as learning by doing. The teacher creates situations in which the student encounters a problem, investigates it, tests possible solutions and reconstructs his or her understanding.
AI radically changes the conditions under which this philosophy operates.
Before AI, there was a practical reason for spending enormous amounts of schooling on acquiring information and routine intellectual techniques. A student needed vocabulary, facts, algorithms, grammar, computational techniques, references, etc., because these were the tools required for subsequent intellectual work. With a powerful AI available, much of this intermediate work can be supplied almost instantly.
That pushes education toward Dewey. Consider mathematics. Instead of spending weeks learning a particular computational procedure and then being given an artificial exercise, a pupil might start with a genuine question:
Why does a suspension bridge have the shape it does?
AI can supply calculations, simulations, explanations at different levels, historical background and even computer programs. The student can formulate hypotheses, challenge the AI, modify the model and compare predictions with observations. This is recognizably Deweyan inquiry—but enormously amplified.
Yet there is a paradox. AI can also destroy Deweyan learning if it does the inquiry for the student.
Suppose the assignment is: Investigate why the Roman Republic collapsed. A student asks an AI and receives a beautifully organized essay. The product may be excellent, but virtually no education has occurred. The student has neither investigated nor struggled with competing explanations nor constructed an argument.
So the distinction in AI education becomes:
AI doing the task versus AI enlarging the student's capacity to do the task.
This makes Dewey's concept of inquiry more important than the familiar slogan learning by doing. Merely producing something—a report, presentation, program or mathematical proof—is no longer evidence that the student has learned anything. AI can produce all of these.
Consequently, I think the AI landscape points toward a modified Deweyan school with three components:
- A substantial foundation of knowledge. A person cannot critically interrogate an AI about Roman history, mathematics or physics without already knowing something. AI therefore does not make knowledge unnecessary. In some respects it makes internal knowledge more important, because otherwise one cannot recognize plausible nonsense.
- Authentic inquiry. Students should increasingly be given difficult questions whose answers aren't simply contained in the textbook. AI becomes an intellectual instrument—something like an extraordinarily powerful library, calculator and conversational research assistant combined.
- Demonstrated understanding rather than submitted products. Assessment would increasingly have to involve conversation, oral examination, explanation at the blackboard, modification of an argument, criticism of an AI answer, or solving a new variation of a problem. The crucial question becomes: What can the student understand and defend?
This also changes the teacher's position. The teacher becomes somewhat more Deweyan and less like an information distributor, but not less important. The teacher chooses worthwhile problems, detects superficial understanding, asks the decisive follow-up question and possesses enough subject knowledge to know when both pupil and AI have gone astray.
There is therefore an interesting historical reversal. During the twentieth century, Dewey could sometimes be interpreted as supporting a movement away from demanding subject knowledge toward activities, projects and pupil-centered learning. AI makes that version of Dewey increasingly untenable. Activities and polished products are now almost free.
What remains scarce is understanding, judgment, curiosity and the ability to formulate a good question.
In that sense, AI may produce a more intellectually demanding version of Dewey than the schools influenced by Dewey ever did: learn through genuine inquiry—but know enough to conduct the inquiry yourself.
If you want, I can:
- Explore how AI can assist but not replace student inquiry in education
- Discuss challenges and solutions for assessing student understanding with AI
- Analyze the evolving role of teachers in AI-augmented Deweyan education
fredag 11 september 2026
Two Forms of LLM for Mathematics: Words and Numbers
We have identified two forms of mathematics, (S) symbolic performed with symbols/words and (N) numerical performed by computation with numbers. The forms of AI now taking humanity with surprise are based on Large Language Models LLM exhibiting a formidable capacity to compose texts as strings of words, after training by reading many texts composed by humans over centuries.
The OpenAI proof the Clay Millennium conjecture that solutions to Navier-Stokes equations can develop singularities in finite time, takes the form of a 167 page string of words formally verified to be logically consistent as a consequence of known already proved theorems. The proof does not present the values of the singular solution in numerical form, only that such a thing must exist as a logical consequence of know theorems taking the form a string of words thus an example of (S), exactly what LLMs are designed to do
RealQM is a new form of quantum mechanics developed with Claude as coding agent and producing numerical solutions to concrete problems of quantum mechanics without other input than case specification thus without free parameters asking for observational input. This represents (N) as numerical computation, which is not a string of words but a string of purely computational tasks. It shows that Claude is very capable of producing efficient code performing the tasks specified by an algorithm for numerical solution of the Schrödinger equation of RealQM needed as starting point. This algorithm is not a proof but a list of tasks obeying logics. The list of basic tasks in numerical computation is limited; basically addition and gradient or fixed-point iteration, at least in the context of physics with equations such as Navier-Stokes.
That Claude can code is not the result of reading massive text, but comes from logic combined with knowledge of numerical algorithms. The training as LLM can then help with logic.
So it is maybe not so surprising that AI can produced lengthy proofs of mathematical theorems, may better and more expedient than real top mathematicians, as an LLM. More surprising maybe that AI can code, which ultimately does not need so much of intelligence.
There is an important difference between step-by-step formal verification of a symbolic proof of some mathematical theorem, which can overwhelming for long proofs with many steps, and assessment of the quality of a computed solution which can be done by a posteriori evaluating its residual without checking every step.
A numerical solution can be time-consuming to compute but quick to check. N vs NP.


