Such united atom approximations have been used in simulations of biological membranes.

这种联合原子近似已被用于生物膜的模拟。

OK, so what about approximations?

什么是近似？

So we're going to need approximations.

所以我们需要近似。

To see where these approximations come in.

来看看这些近似从哪参与进来。

So instead you try to find approximations.

相反，你尝试找到近似。

Rather, you reward approximations to it.

相反，你需要对接近行为进行奖励。

Two important approximations that go in here.

这就是两个重要的近似。

And then putting in the right approximations.

然后采用正确的近似。

Quadratic approximations in two dimensions.

二维中的二次逼近法。

Such estimates should be considered only as crude approximations.

上述估算结果只能是粗略的近似值。

Remember, these approximation formulas, they are linear approximations.

记住，这些近似公式是线性近似。

Such linear calculations, of course, can only be very rough approximations.

这一系列的计算可能仅仅是粗略的估计。

These are the two approximations that we'll use when we put these things together.

要用到的两个近似，这是当我们把这些东西放在一起时。

Analytical expressions are obtained on short time and long time approximations.

在短时和长时近似下，得到了解析表达式。

Values are not approximations to prices nor a necessary step in their calculation.

价值并不是价格的近似值，也不是价格计算必须的步骤。

Well we can go look up here, looking at the differential, there are no approximations here.

好的我们可以看这儿,看这个微分方程，这里没有做近似。

So approximations must be made, especially when larger molecules such as proteins are involved.

因此，近似法必须被采用，特别是对于有类似蛋白质这样的大分子参与的化学过程。

Numerical approximations include functional approximations and statistical approximations.

数值逼近包括函数模型逼近和统计模型逼近。

Still, they can be useful as general approximations of who has large fortunes and who doesn't.

不过，这些排行榜还是有用的，可以让你大体了解谁很有钱，谁没那么有钱。

And two approximations that we're going to talk about are ones I already mentioned at the beginning.

我们将要讨论的两个近似，是我在开始已经提过的。

Computer memory, data storage, and data communications have their own rough approximations of Moore's Law.

计算机内存、数据存储设备和数据通讯设备也有着它们自己的类似定律。

Computers memory, data storage, and data communication have their own rough approximations of Moore's Law.

电脑内存、数据储存与传输，都拥有大致类似于摩尔定律的规律。

Functional approximations and statistical approximations have been widely applied in geodetic computation.

函数模型逼近和统计模型逼近在大地测量均有广泛的应用。

If you want strict equalities in approximations means that we replace the function by its tangent approximation.

如果你想要严格的近似等式,那就意味着我们要用切线逼近来取代原函数。

And that turns out, approximations might sometimes be fine, certainly for the small programs we've seen thus far.

结果可能是，近似处理的相当不错，当然是对于一些小项目来说。

I was using what trainers call "approximations," rewarding the small steps toward learning a whole new behavior.

我用的是驯兽师称为“渐进法”的技巧——奖励学习全新行为过程中的每一个小进步。

But Knezevic, Huynh and Patera's approach could make those approximations both more accurate and easier to calculate.

但是Knezevic, Huynh和Patera的方法可以使得这些近似更准确，更容易计算。

Consider physics: Newtonian models were crude approximations of the truth (wrong at the atomic level, but still useful).

以物理为例：牛顿模型是近似真相的模型(牛顿模型在原子层面上是错误的，但是依旧有用)。

The kinds of approximations needed to, for example, simulate a firestorm, were in the past computationally intractable.

要利用这种近似解来模拟大爆炸在以前是不可能实现的。