什么是熵?
What Is Entropy?

原始链接: https://johncarlosbaez.wordpress.com/2024/07/20/what-is-entropy/

标题:理解熵:简化指南 前言 21 世纪初,Twitter 等社交媒体平台开始流行,用户可以交换被称为“推文”的简短消息。 受这个概念的启发,作者创建了一系列推文来解释熵这一物理学的基本概念。 本书是这些推文的扩展。 什么是熵? 熵是指系统内未知或不可预测的信息量,理论上可以通过进一步的知识或分析来发现。 本书重点探讨这个抽象概念,提供清晰和精确的内容,同时提供实际示例。 涵盖的关键主题 本书涵盖了与熵相关的基本主题,包括: * 信息和香农熵 * 最大熵和吉布斯熵原理 *玻尔兹曼分布、温度、凉爽度、期望能量及其与熵的关系 * 均分定理 * 配分函数 * 自由能和熵的联系 * 经典谐波振荡器的熵 * 经典盒中粒子熵 * 经典理想气体熵 本指南避免讨论热力学第二定律等复杂理论及其含义,因为它们需要额外的详细研究。 此外,作者避免深入研究熵在各个领域的作用,如生物过程、黑洞或其他先进领域。 为了保持清晰易懂的演示,作者故意让讨论远离量子力学,尽管需要某些元素(例如普朗克常数)来导出所讨论的熵公式。 整体结构 本书以简单的对话风格呈现,为读者提供了对熵的简洁而深刻的探索,使初学者可以轻松理解,同时仍然满足那些寻求更深入理解的人的需求。 无论您选择先阅读整本书还是单独深入了解每个主题,内容始终引人入胜且信息丰富。 类别: 物理, 概率

科学进步的局限性源于需要广泛专业知识的新领域日益复杂,使得有才华的大学生难以掌握现有知识,更不用说推进了。 由于同行评审的研究论文和博士论文的标准下降,知识的进步受到阻碍,这是由促进过度论文生产而不是智力深度的激励制度推动的。 为了解决这个问题,需要新的模型和抽象来简化复杂的问题,类似于编程语言中提供的简单性,在编程语言中可以开发复杂的计算机系统,而无需完全理解低级细节。 然而,与技术相比,自然科学的这种转变似乎缓慢,可能是由于保护其地位、资金和传统的守门人的抵制。 信息论为理解知识的客观和主观方面提供了一个有价值的工具。 交叉熵根据实际和相信的频率量化观察时经历的平均惊讶,提供了一种评估各种信念系统的相对准确性的方法。 提高初始数据集或假设的质量可以得出更准确的结论。 影响数据分析准确性的关键因素是观测数据集的大小和细节; 不完整或不够详细的数据集可能会掩盖关键模式或关系。 此外,对熵的解释多种多样,并且可能因上下文而异,这凸显了在传达科学思想时仔细定义术语和概念的重要性。 未破碎的鸡蛋与破碎的鸡蛋的例子说明了系统的熵如何反映其组织或无序。 未破碎的鸡蛋具有最小的熵,因为它以高度组织的状态存在,而破碎的鸡蛋由于蛋壳碎片有多种可能的排列而具有最大的熵。 同样,在热力学中,熵指的是系统内的无序性或随机性,是描述传热、化学反应和相变等过程的基本概念。 最后,理解熵需要精确的术语,并且必须考虑守恒定律、可逆性和其他相关因素,以准确描述自然界中发生的现象。
相关文章

原文

I wrote a little book about entropy; here’s the current draft:

What is Entropy?

If you see typos and other mistakes, or have trouble understanding things, please let me know!

An alternative title would be 92 Tweets on Entropy, but people convinced me that title wouldn’t age well: in decade or two few people may remember what ‘tweets’ were.

Here is the foreword, which explains the basic idea.

Foreword

Once there was a thing called Twitter, where people exchanged short messages called ‘tweets’. While it had its flaws, I came to like it and eventually decided to teach a short course on entropy in the form of tweets. This little book is a slightly expanded version of that course.

It’s easy to wax poetic about entropy, but what is it? I claim it’s the amount of information we don’t know about a situation, which in principle we could learn. But how can we make this idea precise and quantitative? To focus the discussion I decided to tackle a specific puzzle: why does hydrogen gas at room temperature and pressure have an entropy corresponding to about 23 unknown bits of information per molecule? This gave me an excuse to explain these subjects:

• information
• Shannon entropy and Gibbs entropy
• the principle of maximum entropy
• the Boltzmann distribution
• temperature and coolness
• the relation between entropy, expected energy and temperature
• the equipartition theorem
• the partition function
• the relation between expected energy, free energy and entropy
• the entropy of a classical harmonic oscillator
• the entropy of a classical particle in a box
• the entropy of a classical ideal gas.

I have largely avoided the second law of thermodynamics, which says that entropy always increases. While fascinating, this is so problematic that a good explanation would require another book! I have also avoided the role of entropy in biology, black hole physics, etc. Thus, the aspects of entropy most beloved by physics popularizers will not be found here. I also never say that entropy is ‘disorder’.

I have tried to say as little as possible about quantum mechanics, to keep the physics prerequisites low. However, Planck’s constant shows up in the formulas for the entropy of the three classical systems mentioned above. The reason for this is fascinating: Planck’s constant provides a unit of volume in position-momentum space, which is necessary to define the entropy of these systems. Thus, we need a tiny bit of quantum mechanics to get a good approximate formula for the entropy of hydrogen, even if we are trying our best to treat this gas classically.

Since I am a mathematical physicist, this book is full of math. I spend more time trying to make concepts precise and looking into strange counterexamples than an actual ‘working’ physicist would. If at any point you feel I am sinking into too many technicalities, don’t be shy about jumping to the next tweet. The really important stuff is in the boxes. It may help to reach the end before going back and learning all the details. It’s up to you.

联系我们 contact @ memedata.com