Rosa's TurbineBenchmark

Average Accuracy: 80.8% → 99.5%

Time: 18.7s

Precision: binary64

Cost: 1728

Math

\[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5 \]

↓

\[\left(\left(3 + \frac{2}{r \cdot r}\right) - \left(r \cdot w\right) \cdot \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(r \cdot w\right)\right)\right) + -4.5 \]

FPCore

(FPCore (v w r)
 :precision binary64
 (-
  (-
   (+ 3.0 (/ 2.0 (* r r)))
   (/ (* (* 0.125 (- 3.0 (* 2.0 v))) (* (* (* w w) r) r)) (- 1.0 v)))
  4.5))

↓

(FPCore (v w r)
 :precision binary64
 (+
  (-
   (+ 3.0 (/ 2.0 (* r r)))
   (* (* r w) (* (/ (+ 0.375 (* v -0.25)) (- 1.0 v)) (* r w))))
  -4.5))

C

double code(double v, double w, double r) {
	return ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5;
}

↓

double code(double v, double w, double r) {
	return ((3.0 + (2.0 / (r * r))) - ((r * w) * (((0.375 + (v * -0.25)) / (1.0 - v)) * (r * w)))) + -4.5;
}

Fortran

real(8) function code(v, w, r)
    real(8), intent (in) :: v
    real(8), intent (in) :: w
    real(8), intent (in) :: r
    code = ((3.0d0 + (2.0d0 / (r * r))) - (((0.125d0 * (3.0d0 - (2.0d0 * v))) * (((w * w) * r) * r)) / (1.0d0 - v))) - 4.5d0
end function

↓

real(8) function code(v, w, r)
    real(8), intent (in) :: v
    real(8), intent (in) :: w
    real(8), intent (in) :: r
    code = ((3.0d0 + (2.0d0 / (r * r))) - ((r * w) * (((0.375d0 + (v * (-0.25d0))) / (1.0d0 - v)) * (r * w)))) + (-4.5d0)
end function

Java

public static double code(double v, double w, double r) {
	return ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5;
}

↓

public static double code(double v, double w, double r) {
	return ((3.0 + (2.0 / (r * r))) - ((r * w) * (((0.375 + (v * -0.25)) / (1.0 - v)) * (r * w)))) + -4.5;
}

Python

def code(v, w, r):
	return ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5

↓

def code(v, w, r):
	return ((3.0 + (2.0 / (r * r))) - ((r * w) * (((0.375 + (v * -0.25)) / (1.0 - v)) * (r * w)))) + -4.5

Julia

function code(v, w, r)
	return Float64(Float64(Float64(3.0 + Float64(2.0 / Float64(r * r))) - Float64(Float64(Float64(0.125 * Float64(3.0 - Float64(2.0 * v))) * Float64(Float64(Float64(w * w) * r) * r)) / Float64(1.0 - v))) - 4.5)
end

↓

function code(v, w, r)
	return Float64(Float64(Float64(3.0 + Float64(2.0 / Float64(r * r))) - Float64(Float64(r * w) * Float64(Float64(Float64(0.375 + Float64(v * -0.25)) / Float64(1.0 - v)) * Float64(r * w)))) + -4.5)
end

MATLAB

function tmp = code(v, w, r)
	tmp = ((3.0 + (2.0 / (r * r))) - (((0.125 * (3.0 - (2.0 * v))) * (((w * w) * r) * r)) / (1.0 - v))) - 4.5;
end

↓

function tmp = code(v, w, r)
	tmp = ((3.0 + (2.0 / (r * r))) - ((r * w) * (((0.375 + (v * -0.25)) / (1.0 - v)) * (r * w)))) + -4.5;
end

Wolfram

code[v_, w_, r_] := N[(N[(N[(3.0 + N[(2.0 / N[(r * r), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(0.125 * N[(3.0 - N[(2.0 * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(N[(N[(w * w), $MachinePrecision] * r), $MachinePrecision] * r), $MachinePrecision]), $MachinePrecision] / N[(1.0 - v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 4.5), $MachinePrecision]

↓

code[v_, w_, r_] := N[(N[(N[(3.0 + N[(2.0 / N[(r * r), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(r * w), $MachinePrecision] * N[(N[(N[(0.375 + N[(v * -0.25), $MachinePrecision]), $MachinePrecision] / N[(1.0 - v), $MachinePrecision]), $MachinePrecision] * N[(r * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -4.5), $MachinePrecision]

Derivation

1. Initial program 80.8%

   \[\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5 \]

2. Simplified 87.4%

   \[\leadsto \color{blue}{\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + -4.5} \]
   Proof

   [Start] 80.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) - 4.5 \]
   sub-neg [=>] 80.8	\[ \color{blue}{\left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{\left(0.125 \cdot \left(3 - 2 \cdot v\right)\right) \cdot \left(\left(\left(w \cdot w\right) \cdot r\right) \cdot r\right)}{1 - v}\right) + \left(-4.5\right)} \]
   associate-/l* [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\frac{0.125 \cdot \left(3 - 2 \cdot v\right)}{\frac{1 - v}{\left(\left(w \cdot w\right) \cdot r\right) \cdot r}}}\right) + \left(-4.5\right) \]
   cancel-sign-sub-inv [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \color{blue}{\left(3 + \left(-2\right) \cdot v\right)}}{\frac{1 - v}{\left(\left(w \cdot w\right) \cdot r\right) \cdot r}}\right) + \left(-4.5\right) \]
   metadata-eval [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + \color{blue}{-2} \cdot v\right)}{\frac{1 - v}{\left(\left(w \cdot w\right) \cdot r\right) \cdot r}}\right) + \left(-4.5\right) \]
   *-commutative [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{\color{blue}{r \cdot \left(\left(w \cdot w\right) \cdot r\right)}}}\right) + \left(-4.5\right) \]
   *-commutative [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \color{blue}{\left(r \cdot \left(w \cdot w\right)\right)}}}\right) + \left(-4.5\right) \]
   metadata-eval [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + \color{blue}{-4.5} \]

3. Applied egg-rr 99.5%

   \[\leadsto \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(r \cdot w\right)\right) \cdot \left(r \cdot w\right)}\right) + -4.5 \]
   Proof

   [Start] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{\frac{1 - v}{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + -4.5 \]
   associate-/r/ [=>] 87.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{1 - v} \cdot \left(r \cdot \left(r \cdot \left(w \cdot w\right)\right)\right)}\right) + -4.5 \]
   add-sqr-sqrt [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{1 - v} \cdot \color{blue}{\left(\sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right)}\right) + -4.5 \]
   associate-*r* [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \color{blue}{\left(\frac{0.125 \cdot \left(3 + -2 \cdot v\right)}{1 - v} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}}\right) + -4.5 \]
   distribute-rgt-in [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{\color{blue}{3 \cdot 0.125 + \left(-2 \cdot v\right) \cdot 0.125}}{1 - v} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   metadata-eval [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{\color{blue}{0.375} + \left(-2 \cdot v\right) \cdot 0.125}{1 - v} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   *-commutative [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + \color{blue}{\left(v \cdot -2\right)} \cdot 0.125}{1 - v} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   associate-*l* [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + \color{blue}{v \cdot \left(-2 \cdot 0.125\right)}}{1 - v} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   metadata-eval [=>] 87.4	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot \color{blue}{-0.25}}{1 - v} \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   associate-*r* [=>] 73.1	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \sqrt{\color{blue}{\left(r \cdot r\right) \cdot \left(w \cdot w\right)}}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   sqrt-prod [=>] 73.1	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \color{blue}{\left(\sqrt{r \cdot r} \cdot \sqrt{w \cdot w}\right)}\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   sqrt-prod [=>] 42.8	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(\color{blue}{\left(\sqrt{r} \cdot \sqrt{r}\right)} \cdot \sqrt{w \cdot w}\right)\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   sqrt-prod [=>] 21.3	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(\left(\sqrt{r} \cdot \sqrt{r}\right) \cdot \color{blue}{\left(\sqrt{w} \cdot \sqrt{w}\right)}\right)\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   add-sqr-sqrt [<=] 37.3	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(\color{blue}{r} \cdot \left(\sqrt{w} \cdot \sqrt{w}\right)\right)\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]
   add-sqr-sqrt [<=] 75.5	\[ \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(r \cdot \color{blue}{w}\right)\right) \cdot \sqrt{r \cdot \left(r \cdot \left(w \cdot w\right)\right)}\right) + -4.5 \]

4. Final simplification 99.5%

   \[\leadsto \left(\left(3 + \frac{2}{r \cdot r}\right) - \left(r \cdot w\right) \cdot \left(\frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(r \cdot w\right)\right)\right) + -4.5 \]

Alternatives

Alternative 1
Accuracy	98.2%
Cost	1864

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ t_1 := 3 + t_0\\ \mathbf{if}\;v \leq -13500000000000:\\ \;\;\;\;-4.5 + \left(t_1 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{elif}\;v \leq 6.6 \cdot 10^{-53}:\\ \;\;\;\;-4.5 + \left(t_1 - \left(r \cdot w\right) \cdot \left(r \cdot \left(0.375 \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_0 + \left(-1.5 - \frac{0.375 + v \cdot -0.25}{1 - v} \cdot \left(r \cdot \left(w \cdot \left(r \cdot w\right)\right)\right)\right)\\ \end{array} \]

Alternative 2
Accuracy	77.0%
Cost	1616

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ t_1 := t_0 + -1.5\\ t_2 := t_0 + \left(-1.5 - 0.375 \cdot \left(\left(r \cdot r\right) \cdot \left(w \cdot w\right)\right)\right)\\ \mathbf{if}\;r \leq -1.4 \cdot 10^{+154}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;r \leq -2 \cdot 10^{-52}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;r \leq 7.2 \cdot 10^{-72}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;r \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 3
Accuracy	77.1%
Cost	1616

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ t_1 := t_0 + -1.5\\ \mathbf{if}\;r \leq -1.4 \cdot 10^{+154}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;r \leq -3.7 \cdot 10^{-56}:\\ \;\;\;\;t_0 + \left(\left(r \cdot r\right) \cdot \left(\left(w \cdot w\right) \cdot -0.375\right) - 1.5\right)\\ \mathbf{elif}\;r \leq 1.2 \cdot 10^{-71}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;r \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_0 + \left(-1.5 - 0.375 \cdot \left(\left(r \cdot r\right) \cdot \left(w \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 4
Accuracy	77.0%
Cost	1616

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ t_1 := t_0 + -1.5\\ t_2 := \frac{\frac{2}{r}}{r} + \left(\left(r \cdot r\right) \cdot \left(\left(w \cdot w\right) \cdot -0.375\right) - 1.5\right)\\ \mathbf{if}\;r \leq -1.4 \cdot 10^{+154}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;r \leq -2 \cdot 10^{-52}:\\ \;\;\;\;t_2\\ \mathbf{elif}\;r \leq 5 \cdot 10^{-78}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;r \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_2\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 5
Accuracy	78.8%
Cost	1616

\[\begin{array}{l} t_0 := \frac{2}{r \cdot r}\\ t_1 := t_0 + -1.5\\ \mathbf{if}\;r \leq -1.4 \cdot 10^{+154}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;r \leq -7.8 \cdot 10^{-30}:\\ \;\;\;\;\frac{\frac{2}{r}}{r} + \left(\left(r \cdot r\right) \cdot \left(\left(w \cdot w\right) \cdot -0.375\right) - 1.5\right)\\ \mathbf{elif}\;r \leq 6400000000000:\\ \;\;\;\;-4.5 + \left(\left(3 + t_0\right) - 0.25 \cdot \left(w \cdot \left(\left(r \cdot r\right) \cdot w\right)\right)\right)\\ \mathbf{elif}\;r \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;t_0 + \left(-1.5 - 0.375 \cdot \left(\left(r \cdot r\right) \cdot \left(w \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_1\\ \end{array} \]

Alternative 6
Accuracy	82.9%
Cost	1481

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;w \leq -2.1 \cdot 10^{+144} \lor \neg \left(w \leq 4.5 \cdot 10^{+72}\right):\\ \;\;\;\;-4.5 + \left(t_0 - 0.25 \cdot \left(w \cdot \left(\left(r \cdot r\right) \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - 0.375 \cdot \left(r \cdot \left(r \cdot \left(w \cdot w\right)\right)\right)\right)\\ \end{array} \]

Alternative 7
Accuracy	82.9%
Cost	1481

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;w \leq -7 \cdot 10^{+151} \lor \neg \left(w \leq 4.2 \cdot 10^{+72}\right):\\ \;\;\;\;-4.5 + \left(t_0 - 0.25 \cdot \left(w \cdot \left(\left(r \cdot r\right) \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot 0.375\right) \cdot \left(r \cdot \left(w \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 8
Accuracy	97.3%
Cost	1481

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;v \leq -13500000000000 \lor \neg \left(v \leq 4.2\right):\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot 0.25\right) \cdot \left(w \cdot \left(r \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(0.375 \cdot \left(r \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 9
Accuracy	97.3%
Cost	1481

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;v \leq -13500000000000 \lor \neg \left(v \leq 2.1\right):\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot 0.25\right) \cdot \left(w \cdot \left(r \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(r \cdot \left(0.375 \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 10
Accuracy	98.9%
Cost	1481

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;v \leq -13500000000000 \lor \neg \left(v \leq 1\right):\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(w \cdot \left(r \cdot 0.25\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(r \cdot \left(0.375 \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 11
Accuracy	87.9%
Cost	1348

\[\begin{array}{l} t_0 := 3 + \frac{2}{r \cdot r}\\ \mathbf{if}\;v \leq 14500:\\ \;\;\;\;-4.5 + \left(t_0 - \left(r \cdot w\right) \cdot \left(0.375 \cdot \left(r \cdot w\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-4.5 + \left(t_0 - 0.25 \cdot \left(w \cdot \left(\left(r \cdot r\right) \cdot w\right)\right)\right)\\ \end{array} \]

Alternative 12
Accuracy	66.9%
Cost	841

\[\begin{array}{l} \mathbf{if}\;r \leq 5.8 \cdot 10^{+53} \lor \neg \left(r \leq 1.15 \cdot 10^{+76}\right):\\ \;\;\;\;\frac{2}{r \cdot r} + -1.5\\ \mathbf{else}:\\ \;\;\;\;-0.375 \cdot \left(\left(r \cdot r\right) \cdot \left(w \cdot w\right)\right)\\ \end{array} \]

Alternative 13
Accuracy	68.0%
Cost	448

\[\frac{2}{r \cdot r} + -1.5 \]

Alternative 14
Accuracy	40.4%
Cost	320

\[\frac{2}{r \cdot r} \]

Reproduce

herbie shell --seed 2023146 
(FPCore (v w r)
  :name "Rosa's TurbineBenchmark"
  :precision binary64
  (- (- (+ 3.0 (/ 2.0 (* r r))) (/ (* (* 0.125 (- 3.0 (* 2.0 v))) (* (* (* w w) r) r)) (- 1.0 v))) 4.5))