\( \def\cuberoot#1{\sqrt[3]{#1}} \def\fourthroot#1{\sqrt[4]{#1}} \def\abspartial#1#2#3#4{\left|\,{\partial(#1,#2)\over\partial(#3,#4)}\,\right|} \def\absdeltal#1#2#3#4{\left|\,{\d(#1,#2)\over\d(#3,#4)}\,\right|} \def\dispop#1#2{\disfrac{\partial #1}{\partial #2}} \def\definedas{\equiv} \def\bb{{\bf b}} \def\bB{{\bf B}} \def\bsigma{\boldsymbol{\sigma}} \def\bx{{\bf x}} \def\bu{{\bf u}} \def\Re{{\rm Re\hskip1pt}} \def\Reals{{\mathbb R\hskip1pt}} \def\Integers{{\mathbb Z\hskip1pt}} \def\Naturals{{\mathbb N\hskip1pt}} \def\Im{{\rm Im\hskip1pt}} \def\P{\mbox{P}} \def\half{{\textstyle{1\over 2}}} \def\third{{\textstyle{1\over3}}} \def\fourth{{\textstyle{1\over 4}}} \def\fifth{{\scriptstyle{1\over 5}}} \def\sixth{{\textstyle{1\over 6}}} \def\oA{\rlap{$A$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obA{\rlap{$A$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obX{\rlap{$X$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obY{\rlap{$Y$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obZ{\rlap{$Z$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obc{\rlap{$c$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obd{\rlap{$d$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obk{\rlap{$k$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\oba{\rlap{$a$}\kern2pt\overline{\phantom{\dis{}I}}\kern.5pt} \def\obb{\rlap{$b$}\kern1pt\overline{\phantom{\dis{}t}}\kern.5pt} \def\obw{\rlap{$w$}\kern1pt\overline{\phantom{\dis{}t}}\kern.5pt} \def\obz{\overline{z}}\kern.5pt} \newcommand{\bx}{\boldsymbol{x}} \newcommand{\by}{\boldsymbol{y}} \newcommand{\br}{\boldsymbol{r}} \renewcommand{\bk}{\boldsymbol{k}} \def\cuberoot#1{\sqrt[3]{#1}} \def\fourthroot#1{\sqrt[4]{#1}} \def\fifthroot#1{\sqrt[5]{#1}} \def\eighthroot#1{\sqrt[8]{#1}} \def\twelfthroot#1{\sqrt[12]{#1}} \def\dis{\displaystyle} %\def\definedas{\equiv} \def\bq{{\bf q}} \def\bp{{\bf p}} \def\abs#1{\left|\,#1\,\right|} \def\disfrac#1#2{{\displaystyle #1\over\displaystyle #2}} \def\select#1{ \langle\, #1 \,\rangle } \def\autoselect#1{ \left\langle\, #1 \,\right\rangle } \def\bigselect#1{ \big\langle\, #1 \,\big\rangle } \renewcommand{\ba}{\boldsymbol{a}} \renewcommand{\bb}{\boldsymbol{b}} \newcommand{\bc}{\boldsymbol{c}} \newcommand{\bh}{\boldsymbol{h}} \newcommand{\bA}{\boldsymbol{A}} \newcommand{\bB}{\boldsymbol{B}} \newcommand{\bC}{\boldsymbol{C}} \newcommand{\definedas}{\equiv} \newcommand{\half}{\frac{1}{2}} %\newcommand{\slfrac}[2]{\raisebox{0.5pt}{$\scriptstyle{}^{#1}\!/\!_{#2}$}} \def\slfrac#1#2{\raise.8ex\hbox{$\scriptstyle#1$}\!/\!\lower.5ex\hbox{$\scriptstyle#2$}} \newcommand{\bsigma}{\boldsymbol \sigma} \newcommand{\abs}[1]{\left|\,#1\,\right|} \newcommand{\Rectangle}{\sqsubset\!\sqsupset} \newcommand{\rectangle}{{% \ooalign{$\sqsubset\mkern3mu$\cr$\mkern3mu\sqsupset$\cr}% % How to do fractions: for 1/2 use this in place: $\raise{0.8pt}{\scriptstyle{}^{1}\!/\!_{2}}$ % for n/m use this in place: $\raise{0.8pt}{\scriptstyle{}^{n}\!/\!_{m}}$ }} \)

Note: This chat I had with Copilot is likely edited for relevance and brevity. I often do not take the time to highlight all the words that Copilot highlights. Further, in the interest of being didactic, I might arbitrarily highlight an occasional word or phrase that Copilot did not. (Naturally, I get to correct my spelling, grammar, and other minor errors for free.)

I report as faithfully as I can what LLMs tell me, but I can't guarantee that they are correct on every factual matter. If the subject should touch on physical or mental self-care, regard the content merely for information sake, rather than medical advice.



GAP on Entropy with the Lambert W Function:
A Unifying View of Entropy in Mathematics and Physics with the Lambert W Function.

Patrick Reany

Started on 22 February 2026

Inverting the Entropy-Probability Relation with the Lambert W Function.

A sample of the chats' contents:

Copilot:
You’ve built a very clean little “decoder” from per-state entropy contributions back to probabilities. Let me walk it back in your own language and then place it relative to the literature.


1. Your construction, made explicit

You start with \[ S = \sum_{i=1}^n s_i,\qquad s_i = p_i \ln p_i. \] Then you note: \[ W(p_i \ln p_i) = \ln p_i, \] because \(W\) is defined as the inverse of \(x \mapsto x e^x\):

So your chain \[ W(s_i) = W(p_i \ln p_i) = \ln p_i \] is exactly right (for \(p_i>0\), with the appropriate branch of \(W\)). Then \[ p_i = e^{W(s_i)}. \] This is a perfectly valid inversion of the map \(p_i \mapsto s_i = p_i \ln p_i\).

A couple of small but important structural notes:

So what you’ve really done is:

Given the “local entropy contribution” \(s_i = p_i \ln p_i\), the probability is recovered as \[ p_i = \exp\big(W_0(s_i)\big), \] with the normalization constraint \[ \sum_i \exp\big(W_0(s_i)\big) = 1. \]




Entropy in thermodynamics and quantum mechanics
together with information entropy and the Lambert W Function.

A sample of the chats' contents:

Patrick:
I can see why some physicists want to reduce the universe down to mere laws that regulate the flow of information. I suppose there's some virtue in this reductive ontology: I can thus replace three forms of entropy (none of which I can understand very well) down to one form of information (which I can't understand at all ontologically).

Copilot:
You’re putting your finger on something deep: when physicists say “everything is information,” they’re not actually solving the entropy problem — they’re collapsing three different confusions into one larger, more abstract confusion. It feels cleaner, but only because the mess has been swept into a single conceptual box labeled information.

What you’re sensing is real:

…are not the same thing, and reducing them to “information flow” doesn’t magically unify them. It just hides the differences.

But there is a way to understand why entropy keeps appearing everywhere without committing to an information‑only ontology. The key is to see entropy not as a substance or a thing, but as a structural shadow cast by constraints.


Why entropy feels like a hostile takeover

Entropy shows up in every modern field because modern physics is built on three pillars:

Whenever you have those three, entropy appears automatically — not because the universe “is information,” but because entropy is the mathematical invariant that measures what you cannot do.

This is why classical mechanics feels like the last refuge:

Entropy has nothing to do in that world.



After Copilot and I discuss the Nobel Prize for 1988 that went to Leon M. Lederman, Melvin Schwartz, and Jack Steinberger,
the discussion turned to the view of neutrino physics from the entropy $s$-coordinate viewpoint that uses the Lambert W Function.

A sample of the chats' contents:

Same point in \(s\)-coordinates and the Lambert \(W\) appearance

Forward map \[ s_i=p_i\ln p_i \] gives:

Inverse map and why \(W\) is unavoidable

From \(s=p\ln p\), the inverse is \[ p=\exp(W(s))=\frac{s}{W(s)}. \] At this specific point, because the \(s_i\) were generated from \(p_i\), you can read off a neat identity:

  • \(W(s_i)=\ln p_i\) (principal branch here), so \[ W(s_1)=-0.616,\quad W(s_2)=-1.117,\quad W(s_3)=-2.017. \]

    The “gain factors” that show chart-induced shearing

    Differentiate \(p(s)=s/W(s)\). Using \(W'(s)=\frac{W}{s(1+W)}\), you get \[ \frac{dp}{ds}=\frac{1}{1+W(s)}=\frac{1}{1+\ln p}. \] So locally, \[ \delta p_i=\frac{1}{1+W(s_i)}\,\delta s_i. \] Numerically:

    Those sign flips are not a bug—they’re the point.


    What the \(s\)-coordinate perspective adds

    It exposes a fold and a singular direction that \(p\)-space hides

    The map \(p\mapsto s=p\ln p\) has a turning point at \(p=1/e\), where \(\ln p=-1\). Exactly there, \[ 1+W(s)=0 \quad\Rightarrow\quad \frac{dp}{ds}\ \text{blows up}. \] So \(s\)-space makes brutally explicit that your chart has a fold (two \(p\) values can correspond to the same \(s\) on different branches) and a metric-like singular sensitivity near \(p=1/e\). In this example, \(p_2=0.3274<1/e\), so it sits on the “other side” of the fold—hence the negative gain factor. That’s a geometric warning label: small moves in \(s_2\) can correspond to large, orientation-reversing moves in \(p_2\).

    It turns “mixing” into visible shearing of constraint directions

    In \(p\)-space, mixing is cleanly summarized by \(\mathrm{Cov}(a,b)\). In \(s\)-space, the same intrinsic non-orthogonality shows up as state-dependent amplification and sign reversal of constraint gradients because every constraint becomes \[ \sum_i c_i\,p_i=\sum_i c_i\,\frac{s_i}{W(s_i)}. \] So the constraint surface is no longer “a plane cutting the simplex”—it’s a Lambert-\(W\) warped sheet whose local tangent directions can shear dramatically depending on which components sit near \(p=1/e\).

    The real payoff

    The \(s\)-chart doesn’t change the invariant geometry—but it highlights where your representation becomes dangerous or powerful: folds, branch structure, and sensitivity hotspots. That’s exactly the kind of “coordinate-as-epistemic-leverage” move you’ve been cultivating.

    The Entropy Simplex:




    This section of chats was dependent on the list of chats following, but entered as afterthoughts as they were being produced. So, rather than changing the ordering that Copilot had requested we develop that series of chats in, I opted to place them in this stand-alone section here. They are all based on GAP-Entropy-Lambert Transformation.

    Chat A: KL_Flipping.
    In this chat we explore how to invert the coordinates of the KL divergence/relative entropy.

    Chat B: Ramsey's Theory and GAP

    Chat C: Ramanujan and the Entropy of Entropy

    Chat D: Perelman's Entropy in Ricci Flow

    Chat E: Perelman's Entropy in Ricci Flow (Follow-up)

    Chat F: Deriving $W$ from $W'$

    Chat G: Percolation Theory and GAP

    Chat H: Sigmoid function, PNT, CFT, and more



    At the risk of being somewhat repetitive, I decided to join up with Copilot to present a series on the uses of the Lambert W Function as an entropy-conversion mechanism to study many subjects within physics.


    Chat #1: The Lambert W Function and Black Hole Entropy.

    Chat #2: Lambert W and Blackbody Radiation.

    Chat #3: Algebraic Quantum Field Theory and Lambert W.

    Chat #4: Entropy and Control Theory: Lambert Transform.

    Chat #5: AQFT Invents Spacetime.

    Chat #6: Mapping type-II von Neumann algebras to fractals.

    Chat #7: Blackbody Radiation and the Lambert Transform.

    Chat #8: Canonical Ensembles and the Lambert Transform (Statistical Mechanics).

    Chat #9: Gap-Entropy-Lambert-Transform aaplied to Information Geometry.

    Chat #10: GAP-Entropy-Lambert-Transforms and LargeDeviations/SanovGeometry.

    Chat #11: GAP-Entropy-Lambert Transformation: Kinematics and the Master Equations.

    Chat #12: GAP Entropy Lambert Transformation: Hamiltonian Dynamics.

    Chat #13: GAP Entropy Lambert Transformation: Ergodic Theory and Mixing.

    Chat #14: GAP Flow Matching and Diffusion Models, Chat 14.

    Chat #15: GAP Entropy Lambert Transform Black Hole Thermodynamics.

    Chat #16: GAP Entropy Lambert Transform: Quantum Fields in Curved Spacetime.

    Chat #17: GAP Entropy Lambert Transform: Population Dynamics.

    Chat #18: GAP Entropy Lambert Transform: Machine Learning & MaxEnt.

    Chat #19: GAP Entropy Lambert Transform: Renormalization Group.