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单光子探测技术应用_我如何最终在光学/光子学应用程序中使用机器学习作为博士学位

發布時間：2023/12/15 编程问答 33 豆豆

生活随笔收集整理的這篇文章主要介紹了单光子探测技术应用_我如何最终在光学/光子学应用程序中使用机器学习作为博士学位小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

單光子探測技術應用

A few years ago, I secured a scholarship to pursue a PhD in Photonics modelling. Modelling and optimization of optical/photonics waveguides are very important for many upcoming applications, like optical waveguides used for guiding light in Augmented Reality applications. Seeing such cool applications motivated me a lot to start my PhD journey.

幾年前，我獲得了獎學金，以攻讀光子學建模博士學位。光學/光子波導的建模和優化對于許多即將到來的應用非常重要，例如在增強現實應用中用于引導光的光波導。看到如此出色的應用程序激勵了我很多，從而開始了我的博士學位之旅。

Like any other PhD researcher, I went to one place where everyone goes when they are not sure what they are looking for and how to find what they are looking for. Yes, you guessed it right — GOOGLE!!

像任何其他博士研究人員一樣，我去了一個地方，每個人都不確定自己在尋找什么以及如何找到他們想要的東西。是的，您猜對了-GOOGLE！

Luckily for me, I already knew that I was looking for some sort of open-source codes/libraries or commercial software to get my hands dirty with photonics modelling applications.

對我來說幸運的是，我已經知道我正在尋找某種開源代碼/庫或商業軟件，以使我的手與光子建模應用程序隔離。

令我驚訝的是，我發現了…… (To my surprise what I found…)

Commercial softwares- Comsol/Lumerical were expensive for me to buy at that time, and I was not sure to even ask my supervisor to buy as I was not sure which one I will need.
商業軟件 -當時，我購買的Comsol / Lumerical 軟件價格昂貴，而且我不確定要購買哪一款，因此我什至不確定要問主管。
Open-source libraries- There are some open-source libraries like Meep, but I think we need to have more. Google, Facebook, Microsoft all adopting this new trend of making their technologies open source for all to learn. But are the big companies in the optical modelling domain ready to go open-source?
開源庫-有一些開源庫，例如Meep，但我認為我們需要更多。 Google，Facebook，Microsoft都采用了這種新趨勢，即將其技術開放給所有人學習。 但是，光學建模領域的大公司是否準備好開源？
Fortran- I never bothered to learn about Fortran during my Bachelor’s, in fact, I can say that I chose not to hear or learn about Fortran as it was such an outdated language. The syntax is bad, no in-build plotting library, among other problems. Guess what? People still use it because it is fast and a lot of codes which are still being used were written in Fortran. But still Fortran…
Fortran-在我的學士期間，我從不費心去了解Fortran，實際上，我可以說我選擇不聽或不了解Fortran，因為它是一種過時的語言。語法不好，沒有內置繪圖庫，還有其他問題。原因是什么？人們仍然使用它，因為它速度很快，并且仍在使用的許多代碼是用Fortran編寫的。 但仍然是Fortran ...
No optical engineers/scientists community- This hurted me the most. I couldn’t find any blog/website where optical engineer come and share their work and experiences regularly. Till date, I am looking for such websites sharing regular traffic where people talk about photonics (Let me know if you know any such place!).
沒有光學工程師/科學家社區 -這對我造成的傷害最大。我找不到任何光學工程師來定期分享其工作和經驗的博客/網站。直到現在，我正在尋找這樣的網站，這些網站共享經常的流量，人們談論光子學(讓我知道您是否知道這樣的地方！)。

機器學習-流行語… (Machine Learning- the Buzz word…)

If you are reading this then there is a good chance that you have heard about today’s technologies buzz words-

如果您正在閱讀本文，那么您很有可能聽說過當今的技術流行語-

Artificial Intelligence, Machine Learning, Deep Learning

人工智能，機器學習，深度學習

Everyone is talking about it. Every new company is using it or wanting to use it. It doesn’t matter whether they need it or not, but they want to use it. Or atleast show that they are up to date with new technologies and making our life easier and better. I guess it is good marketing as well!!

每個人都在談論它。每個新公司都在使用它或想要使用它。是否需要它并不重要，但他們想使用它。或至少表明它們與新技術保持同步，使我們的生活更加輕松和美好。 我想這也是一個很好的市場！

Having heard these buzz words several times from Mark Zuckerberg, Bill Gates, Tim Cook, I thought there is no harm in looking for what exactly is this machine learning?

在聽過馬克·扎克伯格，比爾·蓋茨，蒂姆·庫克的這些熱門詞匯后，我認為尋找這種機器學習到底是什么沒有害處？

What the hell is machine learning? Then I understood machine learning is actually statistics plus a lot of other things.

機器學習到底是什么？ 然后我了解到機器學習實際上是統計信息以及許多其他事情。

What the hell is machine learning? Credit: https://www.meme-arsenal.com/en/create/meme/1868835機器學習到底是什么？信用： https : //www.meme-arsenal.com/cn/create/meme/1868835

I started with free courses by Prof. Andrew Ng from Stanford and checking for some YouTube videos. I ended up spending more than ￡1000 doing courses from Udacity and Udemy. For a few months, these courses consumed my every weekend. Literally every weekend. I even had to say no to friends a few times to join for a party. Can you imagine!! I know it was stupid of me. Anyway, it is in the past now…

我先從斯坦福大學的吳安德教授提供免費課程，然后查看一些YouTube視頻。我最終花了1000多英鎊從Udacity和Udemy上課。幾個月以來，這些課程每個周末都消耗了我。從字面上看每個周末。我什至不得不幾次拒絕朋友參加聚會。你可以想象！！我知道這很愚蠢。無論如何，現在已經過去了……

機器學習+光子學… (Machine Learning + Photonics…)

First-year into my PhD, I was still looking for a topic which will become my PhD dissertation. Spent months reading various photonics research papers, understanding some initial optical modelling codes with as little help as possible and irritated with almost non-existent online optical scientist community.

進入博士學位的第一年，我仍在尋找一個將成為我的博士學位論文的主題。花了幾個月的時間閱讀各種光子學研究論文，在幾乎沒有幫助的情況下理解了一些初始光學建模代碼，并且對幾乎不存在的在線光學科學家社區感到不快。

And that’s when it clicked me- Shall I try to use Machine Learning for Optical/Photonics Applications?

這就是單擊它的時候-我是否應該嘗試將機器學習用于光學/光子學應用程序？

Yeah, why not!! There is a big online community for machine learning and I had a background in optical engineering. I thought it might work. But remember I was still new in the Machine Learning field and barely scratched the surface to understand and use it efficiently. Then the questions start arising if I had to make it work-

是的，為什么不呢！ 有一個大型的機器學習在線社區，我有光學工程背景。我認為這可能有效。但是請記住，我仍然是機器學習領域的新手，幾乎沒有摸索一下內容就可以有效地理解和使用它。然后，如果我必須使其起作用，就會開始出現問題-

Is it possible or not? Has anybody done it before?
有可能嗎？有人做過嗎？
I was not sure which optical application to consider to use Machine Learning with.
我不確定要與機器學習一起使用的光學應用程序 。
Dataset- There are no open-source datasets available for any optical application. Again no big online optical community.
Dataset-沒有可用于任何光學應用的開源數據集。再次沒有大型在線眼鏡社區。
Coding help- Is there any initial code available to start with? Or do I have to write everything on my own?
編碼幫助-是否可以使用任何初始代碼開始？還是我必須自己編寫所有內容？
The Biggest question- Is it really useful to use Machine Learning with optical, or I just wanted to use the buzz word technology in my work?
最大的問題 -將機器學習與光學結合使用真的有用嗎，還是我只是想在工作中使用流行詞技術？

這個想法在風中消失了將近6個月…… (The idea lost in the wind for almost 6 months…)

With the above questions in mind, I gave up on the idea of using Machine Learning in Photonics. I guess this happens to everybody when they look for PhD topics. Every day, they come up with new ideas and then move on. Same happened to me and I got busy in typical optical/photonics application problems.

考慮到以上問題，我放棄了在光子學中使用機器學習的想法。我想每個人在尋找博士學位主題時都會發生這種情況。每天，他們提出新的想法，然后繼續前進。同樣的事情發生在我身上，我忙于典型的光學/光子學應用問題。

It was a regular workday and maybe I was getting bored and started checking Google News. I exactly don’t remember. I saw a research paper getting published, which used Machine Leaning in some Photonic Power Splitters problem. And I remember saying this to myself- Oh Shit! You thought about it a few months back and it is possible… and it is not only about my excitement of using buzz word technology with optical applications.

這是一個正常的工作日，也許我感到無聊并開始查看Google新聞。我完全不記得了我看到了一篇研究論文發表，該論文在一些光子功率分配器問題中使用了機器傾斜。我記得自己對我說過- 哎呀！ 您幾個月前就考慮過，并且有可能……這不僅是因為我對在光學應用中使用流行詞技術感到興奮。

I can also use it and WILL USE IT…. All my doubts/questions were answered by itself…

我也可以使用它，并會使用它…。 我所有的疑問/問題都得到了回答……

Photo by Austin Chan on Unsplash由Austin Chan 攝于Unsplash

最后的疑問... (Last Unanswered Doubt…)

Datasets?

數據集？

I still didn’t have any dataset available online. Then I thought if I find dataset online then I had to stick to that optical problem. But what if I can generate my own dataset depending on the problem I am interested in. This leads to a 1-month-long process of data collection on my own. It was around 10000 data-points, still far less than a general Machine Learning problem.

我仍然沒有在線可用的任何數據集。然后我想如果我能在線找到數據集，那我就不得不堅持那個光學問題。 但是，如果我可以根據自己感興趣的問題生成自己的數據集，那該怎么辦。這將導致我自己進行為期1個月的數據收集過程。它大約有10000個數據點，仍然遠遠少于一般的機器學習問題。

What I want to say is if you are interested in a problem to use Machine Learning with, you can collect your own data. But again it depends on the type of problem you are interested in. In my case, it worked as I could collect it using our in-house developed code and fabrication facilities available in the research lab.

我想說的是，如果您對使用機器學習的問題感興趣， 可以收集自己的數據 。但這又取決于您感興趣的問題的類型。就我而言，它可以正常工作，因為我可以使用研究實驗室提供的內部開發代碼和制造設施來收集問題。

一百萬美元的問題… (A Million Dollar Question…)

I have been asked 3 times, during my final viva and 2 job interviews- Why to use Machine Learning with an Optical Application problem if we can use equations to predict or optimize a particular optical device?

在最后一次面試和兩次面試中，我被問過3次了。如果我們可以使用方程式來預測或優化特定的光學設備，為什么要將機器學習應用于光學應用問題？

Machine Learning shines when there are a lot of input parameters to be optimized. First, if in our optical problem there are for example more than 10 input device dimensions to be optimized then we can easily employ machine learning. Comparing and optimizing 10 or more parameters one by one yourself is a tough job. Secondly, what if the data which we collected experimentally has some unknown noise factor which is never taken care of in equations. In this case, machine learning can be used to predict new values which will take into account the unknown noise of the experimental kit. So I think these 2 factors/cases make the application on machine learning in an optical problem very useful.

當有很多要優化的輸入參數時，機器學習就會大放異彩。首先，如果在我們的光學問題中，例如有10個以上的輸入設備尺寸需要優化，那么我們可以輕松地采用機器學習。自己一個人比較和優化10個或更多參數是一項艱巨的工作。其次，如果我們通過實驗收集的數據具有一些 未知的噪聲因子，而該噪聲因子在方程中從未涉及到呢？ 在這種情況下，機器學習可用于預測新值，該值將考慮實驗套件的未知噪聲。因此，我認為這兩個因素/情況使得在光學問題上的機器學習應用非常有用。

Later on, I published 2 journal papers out of this work and 1 was even selected for Editor’s Pick. Understanding Machine Learning which started as a curiosity for me ended up being the topic of my PhD. So you never know…

后來，我發表了2篇關于此工作的期刊論文，其中1篇甚至被選為《 編輯推薦》。 對機器學習的好奇開始于我的好奇心，最終成為我博士的主題。所以你永遠不知道...

As Steve Jobs said- You can only connect the dots looking backwards.

正如史蒂夫·喬布斯(Steve Jobs)所說-您只能將點向后看。

How to use Machine Learning in Optical, check the below article-

如何在Optical中使用機器學習，請查看以下文章-

翻譯自: https://towardsdatascience.com/how-i-used-machine-learning-in-optics-photonics-optoelectronics-9452fe332a9f