# Scisprint 2022 July

###### tags: `scisprint`

Sprint infomation (time and location): https://sciwork.dev/sprint/2022/07-taipei

**Date:** 23th July, Saturday, 2022
**Time:** 13:00 -- 17:00 (4 hours)

# Attendees (sign-up)

The venue requires users to provide names. Please sign up using real names by 7/20.

1. Yung-Yu Chen ([@yungyuc](https://twitter.com/yungyuc/))
2. Matt Wang ([@mattwang44](https://twitter.com/mattwang44))
3. Jocelyn Chang (@PerfectlyJoJo#2269)
4. Sophi
5. Yi-En Chou
6. Ting-Yu Chuang
7. CK Lee
8. Cheng-Ze Jiang
9. Yung-Hsiang Hsu
10. Chang-Teng Lin
11. Yi-Xian Chen
12. Weber Wang

# Projects

## pydoccht

Python 官方說明文件臺灣繁體中文翻譯計畫: https://github.com/python/python-docs-zh-tw

Leader: Matt Wang ([@mattwang44](https://twitter.com/mattwang44))

Participants (also add your names here):

1.

## modmesh

https://github.com/solvcon/modmesh

Leader: Yung-Yu Chen ([@yungyuc](https://twitter.com/yungyuc/))

Participants (also add your names here):

1. Yi-En
2. Ting-Yu

### modmesh project schedule

13:00 -- 14:30 Free discussions and coding
14:30 -- 14:40 EN presentation
14:40 -- 14:50 EN Q&A
14:50 -- 15:10 Project progress update (yyc)
15:10 -- 17:00 Free discussions and coding

## Cytnx

Cytnx is a scientific library which aims to provide similar experience as using numpy/scipy/pytorch.
Thus, although Cytnx is written in C++, there's python binding for users familiar with python.
On the other hand, it aims to provides many efficient methods to manipulate tensors, tensor networks, linear algebra. All the critical calculation are come with CPU and GPU dedicated implementations to acheive fully usage of computer resources.

https://github.com/kaihsin/Cytnx

Leader: Chang-Teng Lin

Participants (also add your names here):

1.

## Modulus

NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems

https://docs.nvidia.com/deeplearning/modulus/index.html

Leader: CK Lee

Participants (also add your names here):

1.

# Venue

**Address:** 國家理論科學研究中心物理組
10617 台北市大安區羅斯福路四段一號 次震宇宙館 ([Google Map](https://goo.gl/maps/qp1eAzYwHsLHVYjM6))
3rd floor, room 307

**Rules:**
1. Please do not eat food in the room
2. **Attendees need to provide your name before 7/20**, contact jeffry1829#0568 on the [discord server](https://discord.gg/6MAkFrD) if you have any questions
3. You may register an eduroam account for you to use ntu_peap wifi (if you can't register eduroam account, that's fine. The room has it's own wifi)
For example, as ntu alumni, register at [this site](https://ccnet.ntu.edu.tw/wireless/ccnet/wireless.php?page=eduroam).

## Direction

Use MRT to 台電大樓站, get out from exit no.2, and then walk along 辛亥路一段。
^This is MRT exit no.2, walk along orange arrow direction.(The road is now under construction) You'll encounter 古亭國小, 大三巴洗車店, 新民國小, and then arrive this intersection.
^The road is now under construction
^Go to orange circle spot, walk right straight. And then you'll encounter 綜合體育館
^This red building is 綜合體育館。Walk left straight, you'll find a gap(orange circle). Walk straight still.
^You'll find this gray building. That's the building you're searching for.
^I'll be waiting at 1st floor of the building, you need to sign up, and then I'll lead the way to 3rd floor, room 307.

The following are some pictures of room 307: