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  "body": "\n## Definition\n\nLet $m \\ge 2$, let $D$ be a diagonal $m \\times m$ matrix with positive diagonal entries, and let $f \\colon \\R^m \\to \\R^m$ be smooth.\nConsider the [[reaction–diffusion system|TAAHWE917M]] $\\partial_t u = D \\Delta u + f(u)$ for $u \\colon \\R^n \\times [0, \\infty) \\to \\R^m$, where $\\Delta$ is the [[Laplacian|0E0B6RA8K0]].\nLet $u^* \\in \\R^m$ satisfy $f(u^*) = 0$ and let $J$ be the [[Jacobian matrix|047NDTEPCV]] of $f$ at $u^*$.\nThe uniform state $u^*$ is *Turing unstable*, or has a *diffusion-driven instability*, if:\n\n1. every [[eigenvalue|0BQAA2BK3Q]] of $J$ has negative real part, so $u^*$ is stable without diffusion;\n2. there is $k \u003e 0$ such that $J - k^2 D$ has an eigenvalue with positive real part.\n\nA *Turing pattern* is a stationary, spatially non-constant solution near $u^*$ that bifurcates from a Turing unstable uniform state.\nIt is typically periodic, with a wave number $k$ as in clause 2.\n\n## Remarks\n\nThe perturbation $u = u^* + e^{\\lambda t} e^{i q \\cdot x} w$ with a wave vector $q \\in \\R^n$, $\\abs{q} = k$, solves the linearised system exactly when $\\lambda$ is an eigenvalue of $J - k^2 D$ with eigenvector $w$, which explains clause 2.\nExample: $m = 2$, $J = \\begin{pmatrix} 1 \u0026 -1 \\\\ 3 \u0026 -2 \\end{pmatrix}$, $D = \\operatorname{diag}(1, 10)$.\nThen $\\tr J = -1 \u003c 0$ and $\\det J = 1 \u003e 0$, so clause 1 holds.\nFor $k^2 = 0.3$, $\\det(J - k^2 D) = (0.7)(-5) + 3 = -0.5 \u003c 0$, so $J - k^2 D$ has a positive real eigenvalue and clause 2 holds.\nNon-example: if $D$ is a multiple $d I$ of the identity, the eigenvalues of $J - k^2 D$ are those of $J$ shifted by $-d k^2$, so clause 2 fails whenever clause 1 holds.\nFor $m = 2$, a necessary condition is that the two diffusion coefficients differ.\n",
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