Could I do something that was continuously differentiable?

我可以做一些连续可微事情吗？

In addition, the settlement curve exhibits neither smooth nor differentiable features.

此外，沉降曲线既不平滑也不易分。

But we are much better at learning differentiable models of the world.

但我们更擅长学习世界可微分模型。

The functions we learn are actually surprisingly differentiable.

我们学习函数实际上出奇地可微。

So it's different than the, optimization over differentiable functions.

这与通过可微分函数进行优化不同。

And now what you have is a differentiable model of, let's say, drag and lift as a function of the shape of that solid.

拥有是一个可微分模型，可以说这个模型阻力和升力作为该固体形状函数。

Yeah, all of that needs to be somewhat differentiable so that you can apply sort of gradient-based learning, which is really what deep learning is about.

嗯， 所有这些都需要有一定可微分性， 以便可以应用基于梯度学习，而这正是深度学习核心。

However, the settlement curves of these two typical continuous beam models (Timoshenko and Euler-Bernoulli) are smooth and differentiable, failing to describe the discontinuous features of shield tunnel settlement.

然而，这两种典型连续梁模型（Timoshenko 和 Euler-Bernoulli）沉降曲线平滑且可微分，未能盾构隧道沉降不连续特征。

Fetched and far in the future to be calling or to be thinking, well, this mathematical function that is differentiable is in fact sentient and so on.

遥不可及且远未来， 以至于我们难以称之为或思考，好吧， 这个可微分数学函数实际上是有感知能力等等。

So it's background knowledge, ability to reason in a way that's differentiable, that is somehow connected, deeply integrated with that background knowledge, or builds on top of that background knowledge.

所以这是背景知识， 能够以可微分方式进行推理，这种推理与背景知识某种程度上是相连， 深度集成，或者是背景知识之上构建。

The settlement curve computed by the proposed method is neither smooth nor differentiable when subjected to the ground surface surcharge, compared with LECM, which is closer to the characteristics of real tunnel settlement.

与LECM相比，所提方法计算沉降曲线地表附加费作用下既不平滑，也不易区分，更接近真实隧道沉降特点。

I should say, not differentiable in the sense that we run backprop through it, but in the sense that our brain has some mechanism for estimating gradients of some kind, and that's what makes us efficient.

我应该说， 这里不可微分并不是指我们通过它运行反向传播，而是指我们大脑有一些机制来估计某种梯度，而这正是使我们高效原因。