added formula table

README.md

@@ -49,16 +49,49 @@ task_ids: []
 ## Dataset Description
 
 - **Homepage:**
-- **Repository:**
+- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
 - **Paper:** Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
-- **Point of Contact:**
+- **Point of Contact:** [Yoshitomo Matsubara](mailto:[email protected]) [Yoshitaka Ushiku](mailto:[email protected])
 
 ### Dataset Summary
 
 Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
 We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not a SR method con (re)discover physical laws from such datasets.
 
-This is the Easy set of our SRSD-Feynman datasets, which consists of 30 different physics formulas.
+This is the Easy set of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:
+
+| ID        | Formula                                                                                     |
+|-----------|---------------------------------------------------------------------------------------------|
+| I.12.1    | \\(F = \mu N_\text{n}\\) |
+| I.12.4    | \\(E = \frac{q_1}{4 \pi \epsilon r^2}\\) |
+| I.12.5    | \\(F = q_2 E\\) |
+| I.14.3    | \\(U = m g z\\) |
+| I.14.4    | \\(U = \frac{k_\text{spring} x^2}{2}\\) |
+| I.18.12   | \\(tau = r F \sin\theta\\) |
+| I.18.16   | \\(L = m r v \sin\theta\\) |
+| I.25.13   | \\(V = \frac{q}{C}\\) |
+| I.26.2    | \\(n = \frac{\sin\theta_1}{\sin\theta_2}\\) |
+| I.27.6    | \\(f = \frac{1}{\frac{1}{d_1}+\frac{n}{d_2}}\\) |
+| I.30.5    | \\(d = \frac{\lambda}{n \sin\theta}\\) |
+| I.43.16   | \\(v = \mu q \frac{V}{d}\\) |
+| I.47.23   | \\(c = \sqrt{\frac{\gamma P}{\rho}}\\) |
+| II.2.42   | \\(J = \kappa (T_2-T_1) \frac{A}{d}\\) |
+| II.3.24   | \\(h = \frac{W}{4 \pi r^2}\\) |
+| II.4.23   | \\(\phi = \frac{q}{4 \pi \epsilon r}\\) |
+| II.8.31   | \\(u = \frac{\epsilon E^2}{2}\\) |
+| II.10.9   | \\(E = \frac{\sigma_\text{free}}{\epsilon} \frac{1}{1+\chi}\\) |
+| II.13.17  | \\(B = \frac{1}{4 \pi \epsilon c^2} \frac{2 I}{r}\\) |
+| II.15.4   | \\(U = -\mu B \cos\theta\\) |
+| II.15.5   | \\(U = -p E \cos\theta\\) |
+| II.27.16  | \\(S = \epsilon c E^2\\) |
+| II.27.18  | \\(u = \epsilon E^2\\) |
+| II.34.11  | \\(\omega = g \frac{q B}{2 m}\\) |
+| II.34.29b | \\(U = 2 \pi g \mu B \frac{J_z}{h}\\) |
+| II.38.3   | \\(F = Y A \frac{\Delta l}{l}\\) |
+| II.38.14  | \\(\mu = \frac{Y}{2 (1+\sigma)}\\) |
+| III.7.38  | \\(\omega = \frac{4 \pi \mu B}{h}\\) |
+| III.12.43 | \\(J = \frac{m h}{2 \pi}\\) |
+| III.15.27 | \\(k = \frac{2 \pi}{N b} s\\) |
 
 
 ### Supported Tasks and Leaderboards
@@ -83,7 +116,7 @@ For each dataset, we have
 1. train split (txt file, whitespace as a delimiter)
 2. val split (txt file, whitespace as a delimiter)
 3. test split (txt file, whitespace as a delimiter)
-4. true equation (pickle file)
+4. true equation (pickle file for sympy object)
 
 ### Data Splits