The　first　fractional　model　for　Reynolds　stresses　in　wall-bounded　turbulent　flows　was　proposed　by　Wen　Chen　[2].　Here,　we　extend　this　formulation　by　allowing　the　fractional　order　alpha(y)　of　the　model　to　vary　with　the　distance　from　the　wall　(y)　for　turbulent　Couette　flow.　Using　available　direct　numerical　simulation　(DNS)　data,　we　formulate　an　inverse　problem　for　alpha(y)　and　design　a　physics-informed　neural　network　(PINN)　to　obtain　the　fractional　order.　Surprisingly,　we　found　a　universal　scaling　law　for　alpha(y(+)),　where　y(+)　is　the　non-dimensional　distance　from　the　wall　in　wall　units.　Therefore,　we　obtain　a　variable-order　fractional　model　that　can　be　used　at　any　Reynolds　number　to　predict　the　mean　velocity　profile　and　Reynolds　stresses　with　accuracy　better　than　1%.