(The chapter on non-linear partial differential equations, to be precise.)

准确地讲是非线性偏微分。

I discover that I'm not very good at geometry, but also that I excel in differential equations.

我发现我不太擅长几何，但我擅长微分。

In this degree, you're gonna have to go through calculus one, calculus two, calculus three, differential equations, a lot of math.

在获取位的过中，你必须习微积分一，微积分二，微积分三，微分，要习大量的数知识。

You know, it was the old quantum mechanics, solving partial differential equations.

你知道，这是旧的量子力，求解偏微分。

Many differential equations students among you might enjoy thinking about how you solve this.

许多习微分的会喜欢思考如何解决这个问题。

So you're just explicitly solving the differential equations that describe the circuit elements.

所以你只是显式地求解描述电路元件的微分。

Equation (4) is a high-order ordinary differential equation, which is not convenient to solve directly.

（4）是一个阶常微分，不便直接求解。

And so essentially I've got a function here to describe the differential equation.

这里我有一个函数来描述这个微分。

And the best paper at this year's symposium was this thing called Neural Ordinary Differential Equations.

今年研讨会的最佳论文是这篇名为《神经常微分》的文章。

So it results in a lot of differential equations that need to be solved simultaneously.

因此，这导致了大量需要同时求解的微分。

They're not into complex differential equations.

他们不擅长复杂的微分。

I mentioned solving PDEs and this iterative process, so we've developed another benchmark called the HPCG benchmark.

我提到了求解偏微分和这个迭代过，所以我们开发了另一个基准测试叫做性共轭梯度基准测试。

And so we were theorizing that, and I wrote a couple of

所以我们当时在理论推导，我写了几道微分，最终算出，根据军队规模，我认为比例必须达到1.7。也就是说，他们需要消灭相当于亡者大军1.7倍数量的士兵，才赢得这场战斗。

So what we found is that the action is minimal for the path that satisfies this curious differential equation.

因此我们发现，对于满足这个奇怪的微分的路径，作用量是最小的。

Then you see that the principle of least action works whenever this differential equation is satisfied.

然后你会发现，只要满足这个微分，最小作用原理就会起作用。

So often with differential equations, it might be attracted to a single point.

所以，对于微分来说，它会被吸引到一个单一的点。

So all of those things usually center on solving the three-dimensional partial differential equations.

因此，所有这些工作通常都围绕着求解三维偏微分展开。

So it matches or tries to imitate what applications do in solving that three-dimensional partial differential equation.

因此，它匹配或试图模仿应用序在求解三维偏微分时所执行的操作。

They derived and solved the differential equation of pipeline deformation under high-fill conditions.

他们推导并求解了填充条件下管道变形的微分。

One differential equation for every synapse, dendrite, or neuron in your system.

系统中的每个突触、树突或神经元都有一个微分。