一、公式王
网站：https://gongshi.wang/，从此再也不用手敲恶心的数学公式了～
应该是利用OCR识别图片中的公式，再将其转换为latex和mathML格式。其中mathML格式粘贴到word中，选择‘仅保留文本’，可以完美的显示，在Office和wps切换中，也没有出现错误。
在我使用的过程中，反应很迅速，也没有出现错误。
希望大家觉得不错时，可以多给作者打赏！。

二、神经网络画图工具
网址：overloaf在线绘图
使用latex格式绘制神经网络，当然也可以使用latex的tikz进行绘图哈。主界面如下：

我用它在线绘制LSTM的效果图：

绘制LSTM网络的latex代码（代码见水印）：
'''
% Kalman filter system model
% by Burkart Lingner
% An example using TikZ/PGF 2.00
%
% Features: Decorations, Fit, Layers, Matrices, Styles
% Tags: Block diagrams, Diagrams
% Technical area: Electrical engineering

\documentclass[a4paper,10pt]{article}

\usepackage[english]{babel}
\usepackage[T1]{fontenc}
\usepackage[ansinew]{inputenc}

\usepackage{lmodern} % font definition
\usepackage{amsmath} % math fonts
\usepackage{amsthm}
\usepackage{amsfonts}

\usepackage{tikz}

%%%<
\usepackage{verbatim}
\usepackage[active,tightpage]{preview}
\PreviewEnvironment{tikzpicture}
\setlength\PreviewBorder{5pt}%
%%%>

\begin{comment}
:Title: Kalman Filter System Model
:Slug: kalman-filter
:Author: Burkart Lingner

This is the system model of the (linear) Kalman filter.

\end{comment}

\usetikzlibrary{decorations.pathmorphing} % noisy shapes
\usetikzlibrary{fit} % fitting shapes to coordinates
\usetikzlibrary{backgrounds} % drawing the background after the foreground

\begin{document}

\begin{figure}[htbp]
\centering
% The state vector is represented by a blue circle.
% "minimum size" makes sure all circles have the same size
% independently of their contents.
\tikzstyle{state}=[circle,
thick,
minimum size=1.2cm,
draw=blue!80,
fill=blue!20]

% The measurement vector is represented by an orange circle.
\tikzstyle{measurement}=[circle,
thick,
minimum size=1.2cm,
draw=orange!80,
fill=orange!25]

% The control input vector is represented by a purple circle.
\tikzstyle{input}=[circle,
thick,
minimum size=1.2cm,
draw=purple!80,
fill=purple!20]

% The input, state transition, and measurement matrices
% are represented by gray squares.
% They have a smaller minimal size for aesthetic reasons.
\tikzstyle{matrx}=[rectangle,
thick,
minimum size=1cm,
draw=gray!80,
fill=gray!20]

% The system and measurement noise are represented by yellow
% circles with a "noisy" uneven circumference.
% This requires the TikZ library "decorations.pathmorphing".
\tikzstyle{noise}=[circle,
thick,
minimum size=1.2cm,
draw=yellow!85!black,
fill=yellow!40,
decorate,
decoration={random steps,
segment length=2pt,
amplitude=2pt}]

% Everything is drawn on underlying gray rectangles with
% rounded corners.
\tikzstyle{background}=[rectangle,
fill=gray!10,
inner sep=0.2cm,
rounded corners=5mm]

\begin{tikzpicture}[>=latex,text height=1.5ex,text depth=0.25ex]
% "text height" and "text depth" are required to vertically
% align the labels with and without indices.

% The various elements are conveniently placed using a matrix:
\matrix[row sep=0.5cm,column sep=0.5cm] {
% First line: Control input
&
\node (u_k-1) [input]{}; &
&
\node (u_k) [input]{}; &
&
\node (u_k+1) [input]{}; &
\
% Second line: System noise & input matrix
\node (w_k-1) [noise] {}; &
\node (B_k-1) [matrx] {}; &
\node (w_k) [noise] {}; &
\node (B_k) [matrx] {}; &
\node (w_k+1) [noise] {}; &
\node (B_k+1) [matrx] {}; &
\
% Third line: State & state transition matrix
\node (A_k-2) {}; &
\node (x_k-1) [state] {}; &
\node (A_k-1) [matrx] {}; &
\node (x_k) [state] {}; &
\node (A_k) [matrx] {}; &
\node (x_k+1) [state] {}; &
\node (A_k+1) {}; \
% Fourth line: Measurement noise & measurement matrix
\node (v_k-1) [noise] {}; &
\node (H_k-1) [matrx] {}; &
\node (v_k) [noise] {}; &
\node (H_k) [matrx] {}; &
\node (v_k+1) [noise] {}; &
\node (H_k+1) [matrx] {}; &
\
% Fifth line: Measurement
&
\node (z_k-1) [measurement] {}; &
&
\node (z_k) [measurement] {}; &
&
\node (z_k+1) [measurement] {}; &
\
};

% The diagram elements are now connected through arrows:
\path[->]
    (A_k-2) edge[thick] (x_k-1) % The main path between the
    (x_k-1) edge[thick] (A_k-1) % states via the state
    (A_k-1) edge[thick] (x_k)       % transition matrices is
    (x_k)   edge[thick] (A_k)       % accentuated.
    (A_k)   edge[thick] (x_k+1) % x -> A -> x -> A -> ...
    (x_k+1) edge[thick] (A_k+1)
    
    (x_k-1) edge (H_k-1)                % Output path x -> H -> z
    (H_k-1) edge (z_k-1)
    (x_k)   edge (H_k)
    (H_k)   edge (z_k)
    (x_k+1) edge (H_k+1)
    (H_k+1) edge (z_k+1)
    
    (v_k-1) edge (z_k-1)                % Output noise v -> z
    (v_k)   edge (z_k)
    (v_k+1) edge (z_k+1)
    
    (w_k-1) edge (x_k-1)                % System noise w -> x
    (w_k)   edge (x_k)
    (w_k+1) edge (x_k+1)
    
    (u_k-1) edge (B_k-1)                % Input path u -> B -> x
    (B_k-1) edge (x_k-1)
    (u_k)   edge (B_k)
    (B_k)   edge (x_k)
    (u_k+1) edge (B_k+1)
    (B_k+1) edge (x_k+1)
    ;

% Now that the diagram has been drawn, background rectangles
% can be fitted to its elements. This requires the TikZ
% libraries "fit" and "background".
% Control input and measurement are labeled. These labels have
% not been translated to English as "Measurement" instead of
% "Messung" would not look good due to it being too long a word.
\begin{pgfonlayer}{background}
    \node [background,
                fit=(u_k-1) (u_k+1),
                label=left:Entrance:] {};
    \node [background,
                fit=(w_k-1) (v_k-1) (A_k+1)] {};
    \node [background,
                fit=(z_k-1) (z_k+1),
                label=left:Measure:] {};
\end{pgfonlayer}

\end{tikzpicture}

\caption{Kalman filter system model}
\end{figure}

This is the system model of the (linear) Kalman filter. At each time
step the state vector is propagated to the new state
estimation by multiplication with the constant state
transition matrix . The state vector is
additionally influenced by the control input vector
multiplied by the input matrix , and the system noise vector
. The system state cannot be measured directly. The
measurement vector consists of the information contained
within the state vector multiplied by the measurement
matrix , and the additional measurement noise .

\end{document}
'''
三、visual-paradigm在线绘图
还有一个在线绘图网站：visual-paradigm

网站有很多好看的模板，可修改性强，主界面：

上面三个就是这次用的很爽的工具了，记录一下😊。
对了，发现一个研究生数学建模论文收集的网址，分享一下：历年研究生数学建模优秀论文汇总
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版权声明：本文为CSDN博主「慕木子」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/MumuziD/article/details/108709537