A fractal set has a Hausdorff dimension.
In some cases, we may generate a fractal by iterating $f,$ 
and let the fractal be the set of starting points $x$ such 
that $|f^{\circ n}(x)|$ is bounded as $n$ grows. (The julia set and the sierpinski triangle are such sets, if one allows $f$ to be a Hutchinson operator).


We may also have an invariant measure, $\mu,$ that is, $\mu(A) = \mu(f^{-1}(A)).$


The support of $\mu$ is the fractal set.


**My question is:** is there a way to modify the "dimension" notion to take this invariant
measure into account somehow? 
Some parts of the fractal might be more dense, and thusly should "contribute more" to the dimension.


An idea would be to use box-counting, but instead of just counting if it is occupied or not,
one uses the invariant measure on the box instead. Has this been studied?



Hausdorff dimension for invariant measure?
There are a wide variety of notions of dimension of a measure. Your basic intuition is completely correct: for a dynamical system, the dimension of a natural invariant measure provides more relevant information than the dimension of the invariant set, since the system may spend more time in certain parts of the space.


For sufficiently homogeneous measures, all reasonable notions of dimension will agree. By ``sufficiently homogeneous'' I mean something very precise: that
$$
C^{-1} \, r^s \le \mu(B(x,r)) \le C\, r^s
$$
for some constant $C\ge 1$, some $s\ge 0$ and all points $x$ in the support of $\mu$. Of course **the** dimension in this case is $s$. Such measures are often called Ahlfors-regular, and an example is the natural measure on the middle-thirds Cantor set.


For more general measures, the **local dimension** is one of the most important concepts and has already been mentioned:
$$
\dim(\mu,x)=\lim\_{r\to 0}\frac{\log \mu(B(x,r))}{\log r}.
$$
But this is really a function of the point $x$ (and not even, as the limit in the definition may not exist, although one can always speak of upper and lower local dimensions).


There are several ways to globalize the information given by the local dimensions. Perhaps the easiest is to take the essential supremum/infimum of the upper/lower local dimensions. This results in four global concepts of dimensions, known as **upper/lower packing/Hausdorff dimensions** of the measure. They turn out (somewhat surprisingly) to be closely connected to the dimensions of the sets the measure ``sees''. For example, the upper Hausdorff dimension of a probability measure $\mu$ (that is, the essential supremum of the lower local dimensions), is the same as the infimum of the Hausdorff dimension of $A$ over all Borel sets $A$ of full measure.


A finer study is provided by the **multifractal spectrum** of a measure $\mu$: for each $\alpha$, we form the level set $E\_\alpha$ of all points $x$ where $\dim(\mu,x)=\alpha$. Then we try to understand how the size of $E\_\alpha$ depends on $\alpha$, for example by studying the function $\alpha\to \dim\_H(E\_\alpha)$.


There are (many!) other useful concepts of dimension which are not directly related to local dimension. In computing lower bounds for the Hausdorff dimension, the potential method is widely applicable: if a measure $\mu$ satisfies that the energy integral
$$
I\_s(\mu) = \int \frac{d\mu(x)\, d\mu(y)}{|x-y|^s}
$$
is finite, then the support of $\mu$ has Hausdorff dimension at least $s$. So it makes sense to think of $\sup\{s: I\_s(\mu)<\infty\}$ as a notion of dimension of $\mu$. This is often called the (lower) **correlation dimension**, and is one instance of a more general family of dimensions indexed by a real number $q$ (correlation dimension corresponds to $q=2$, and has several alternative definitions, perhaps pointing to its importance).


Yet another notion of dimension has a dynamical underpinning. Given a probability measure $\mu$ say on the unit cube $[0,1]^d$, we may consider the entropy $H\_k(\mu)$ of $\mu$ with respect to the partition into dyadic cubes of side length $2^{-k}$. We then define the **entropy (also called information) dimension** of $\mu$ as
$$
\lim\_{k\to\infty} \frac{H\_k(\mu)}{k\log 2}.
$$


This is just a sample of the diverse zoo of dimensions of a measure. Which ones to use depends on the context and what you are able to compute/prove.


Coming back to invariant measures, it is very often the case that the local dimension exists and takes a constant value at almost every point. Such measures are called **exact dimensional**, and have the property that lower and upper Hausdorff dimension, as well as entropy dimension, are all equal to this almost sure value. (But correlation dimension may be strictly smaller, and the multifractal spectrum may still be very rich; in other words, even though attained on a set of measure zero, other local dimensions may still be relevant).


Proving that measures invariant under certain class of dynamics are exact dimensional may be very challenging. Eckmann and Ruelle conjectured in 1984 that hyperbolic measures ergodic a $C^{1+\delta}$ diffeomorphism are exact dimensional. This was proved by Barreira, Pesin and Schmeling in 1999; the paper appeared in Annals.


For invariant measures, there is often a strong connection between their dimension and other dynamical characteristics (at least generically). The conformal expanding case is the easiest: in this case one has the well-known formula ``dimension=entropy/Lyapunov exponent". The nonconformal situation is much harder, but still a lot of deep research has been done, for example Ledrappier-Young theory.


