yoshitomo-matsubara
commited on
Commit
·
f8e8893
1
Parent(s):
2097e13
Add problem table and update README
Browse files- README.md +5 -47
- problem_table.pdf +0 -0
README.md
CHANGED
@@ -60,51 +60,10 @@ We carefully reviewed the properties of each formula and its variables in [the F
|
|
60 |
|
61 |
This is the ***Medium set*** of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:
|
62 |
|
63 |
-
|
64 |
-
|
65 |
-
|
66 |
-
|
67 |
-
| I.11.19 | \\(A = x_1 y_1+x_2 y_2+x_3 y_3\\) |
|
68 |
-
| I.12.2 | \\(F = \frac{q_1 q_2}{4 \pi \epsilon r^2}\\) |
|
69 |
-
| I.12.11 | \\(F = q \left(E + B v \sin\left(\theta\right)\right)\\) |
|
70 |
-
| I.13.4 | \\(K = \frac{1}{2} m (v^2 + u^2 + w^2)\\) |
|
71 |
-
| I.13.12 | \\(U = G m_1 m_2 \left(\frac{1}{r_2}-\frac{1}{r_1}\right)\\) |
|
72 |
-
| I.15.10 | \\(p = \frac{m_0 v}{\sqrt{1-v^2/c^2}}\\) |
|
73 |
-
| I.16.6 | \\(v_1 = \frac{u+v}{1+u v/c^2}\\) |
|
74 |
-
| I.18.4 | \\(r = \frac{m_1 r_1+m_2 r_2}{m_1+m_2}\\) |
|
75 |
-
| I.24.6 | \\(E = \frac{1}{4} m (\omega^2+\omega_0^2) x^2\\) |
|
76 |
-
| I.29.4 | \\(k = \frac{\omega}{c}\\) |
|
77 |
-
| I.32.5 | \\(P = \frac{q^2 a^2}{6 \pi \epsilon c^3}\\) |
|
78 |
-
| I.34.8 | \\(\omega = \frac{q v B}{p}\\) |
|
79 |
-
| I.34.10 | \\(\omega = \frac{\omega_0}{1-v/c}\\) |
|
80 |
-
| I.34.27 | \\(W = \frac{h}{2 \pi} \omega\\) |
|
81 |
-
| I.38.12 | \\(r = 4 \pi \epsilon \frac{\left(h/\left(2 \pi\right)\right)^2}{m q^2}\\) |
|
82 |
-
| I.39.10 | \\(U = \frac{3}{2} P V\\) |
|
83 |
-
| I.39.11 | \\(U = \frac{P V}{\gamma-1}\\) |
|
84 |
-
| I.43.31 | \\(D = \mu k T\\) |
|
85 |
-
| I.43.43 | \\(\kappa = \frac{1}{\gamma - 1} \frac{k v}{\sigma_c}\\) |
|
86 |
-
| I.48.2 | \\(E = \frac{m c^2}{\sqrt{1-v^2/c^2}}\\) |
|
87 |
-
| II.6.11 | \\(\phi = \frac{1}{4 \pi \epsilon} \frac{p \cos\theta}{r^2}\\) |
|
88 |
-
| II.8.7 | \\(U = \frac{3}{5} \frac{Q^2}{4 \pi \epsilon a}\\) |
|
89 |
-
| II.11.3 | \\(x = \frac{q E}{m (\omega_0^2-\omega^2)}\\) |
|
90 |
-
| II.21.32 | \\(\phi = \frac{q}{4 \pi \epsilon r (1-v/c)}\\) |
|
91 |
-
| II.34.2 | \\(\mu = \frac{q v r}{2}\\) |
|
92 |
-
| II.34.2a | \\(I = \frac{q v}{2 \pi r}\\) |
|
93 |
-
| II.34.29a | \\(\mu = \frac{q h}{4 \pi m}\\) |
|
94 |
-
| II.37.1 | \\(E = \mu (1+\chi) B\\) |
|
95 |
-
| III.4.32 | \\(n = \frac{1}{\exp(h \omega/2 \pi k T) - 1}\\) |
|
96 |
-
| III.8.54 | \\(|C|^2 = \sin^2 \frac{2 \pi A t}{h}\\) |
|
97 |
-
| III.13.18 | \\(v = \frac{4 \pi A b^2}{h} k\\) |
|
98 |
-
| III.14.14 | \\(I = I_0 \left(\exp\left(q \Delta V/\kappa T\right)-1\right)\\) |
|
99 |
-
| III.15.12 | \\(E = 2 A (1-\cos k d)\\) |
|
100 |
-
| III.15.14 | \\(m = \frac{h^2}{8 \pi^2 A b^2}\\) |
|
101 |
-
| III.17.37 | \\(f = \beta (1+\alpha \cos\theta)\\) |
|
102 |
-
| III.19.51 | \\(E = -\frac{m q^4}{2 (4 \pi \epsilon)^2 (h/(2 \pi))^2 n^2}\\) |
|
103 |
-
| B8 | \\(U = \frac{E}{1+\frac{E}{m c^2} (1-\cos\theta)}\\) |
|
104 |
-
| B18 | \\(\rho = \frac{3}{8 \pi G} \left(\frac{c^2 k_\text{f}}{a_\text{f}^2}+H^2\right)\\) |
|
105 |
-
|
106 |
-
|
107 |
-
More details of these datasets such as variables and sampling ranges are provided in [the paper and its supplementary material](https://arxiv.org/abs/2206.10540).
|
108 |
|
109 |
### Supported Tasks and Leaderboards
|
110 |
|
@@ -199,7 +158,7 @@ MIT License
|
|
199 |
```bibtex
|
200 |
@article{matsubara2022rethinking,
|
201 |
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
|
202 |
-
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and
|
203 |
journal={arXiv preprint arXiv:2206.10540},
|
204 |
year={2022}
|
205 |
}
|
@@ -211,7 +170,6 @@ Authors:
|
|
211 |
- Yoshitomo Matsubara (@yoshitomo-matsubara)
|
212 |
- Naoya Chiba (@nchiba)
|
213 |
- Ryo Igarashi (@rigarash)
|
214 |
-
- Tatsunori Taniai
|
215 |
- Yoshitaka Ushiku (@yushiku)
|
216 |
|
217 |
|
|
|
60 |
|
61 |
This is the ***Medium set*** of our SRSD-Feynman datasets, which consists of the following 40 different physics formulas:
|
62 |
|
63 |
+
[![Click here to open a PDF file](problem_table.png)](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_medium/resolve/main/problem_table.pdf)
|
64 |
+
|
65 |
+
|
66 |
+
More details of these datasets are provided in [the paper and its supplementary material](https://arxiv.org/abs/2206.10540).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
67 |
|
68 |
### Supported Tasks and Leaderboards
|
69 |
|
|
|
158 |
```bibtex
|
159 |
@article{matsubara2022rethinking,
|
160 |
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
|
161 |
+
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
|
162 |
journal={arXiv preprint arXiv:2206.10540},
|
163 |
year={2022}
|
164 |
}
|
|
|
170 |
- Yoshitomo Matsubara (@yoshitomo-matsubara)
|
171 |
- Naoya Chiba (@nchiba)
|
172 |
- Ryo Igarashi (@rigarash)
|
|
|
173 |
- Yoshitaka Ushiku (@yushiku)
|
174 |
|
175 |
|
problem_table.pdf
ADDED
Binary file (198 kB). View file
|
|