Fix documentation typo

datasets/MHD_256/README.md

@@ -57,7 +57,7 @@ where $\rho$ is the density, $\mathbf{v}$ is the velocity, $\mathbf{B}$ is the m
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** MHD fluid flows in the compressible limit (sub and super sonic, sub and super Alfvenic).
+**What phenomena of physical interest are captured in the data:** MHD fluid flows in the compressible limit (sub and super sonic, sub and super Alfvenic).
 
 **How to evaluate a new simulator operating in this space:** Check metrics such as Power spectrum, two points correlation function.
 

datasets/MHD_64/README.md

@@ -60,7 +60,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** MHD fluid flows in the compressible limit (sub and super sonic, sub and super Alfvenic).
+**What phenomena of physical interest are captured in the data:** MHD fluid flows in the compressible limit (sub and super sonic, sub and super Alfvenic).
 
 **How to evaluate a new simulator operating in this space:** Check metrics such as Power spectrum, two-points correlation function.
 

datasets/active_matter/README.md

@@ -58,7 +58,7 @@ $\zeta =$ {1,3,5,7,9,11,13,15,17}.
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** How is energy being transferred between scales? How is vorticity coupled to the orientation field? Where does the transition from isotropic state to nematic state occur with the change in alignment ( $\zeta$ ) or dipole strength ($\alpha$)?
+**What phenomena of physical interest are captured in the data:** How is energy being transferred between scales? How is vorticity coupled to the orientation field? Where does the transition from isotropic state to nematic state occur with the change in alignment ( $\zeta$ ) or dipole strength ($\alpha$)?
 
 
 **How to evaluate a new simulator operating in this space:** Reproducing some summary statistics like power spectra and average scalar order parameters. Additionally, being able to accurately capture the phase transition from isotropic to nematic state.

datasets/euler_multi_quadrants_openBC/README.md

@@ -76,7 +76,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** capture the shock formations and interactions. Multiscale shocks.
+**What phenomena of physical interest are captured in the data:** capture the shock formations and interactions. Multiscale shocks.
 
 **How to evaluate a new simulator operating in this space:** the new simulator should predict the shock at the right location and time, and the right shock strength, as compared to a pressure gauge monitoring the exact solution.
 

datasets/euler_multi_quadrants_periodicBC/README.md

@@ -71,7 +71,7 @@ with $\rho$ the density, $u$ and $v$ the $x$ and $y$ velocity components, $e$ th
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** capture the shock formations and interactions. Multiscale shocks.
+**What phenomena of physical interest are captured in the data:** capture the shock formations and interactions. Multiscale shocks.
 
 **How to evaluate a new simulator operating in this space:** the new simulator should predict the shock at the right location and time, and the right shock strength, as compared to a pressure gauge monitoring the exact solution.
 

datasets/gray_scott_reaction_diffusion/README.md

@@ -63,7 +63,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** Pattern formation: by sweeping the two parameters $f$ and $k$, a multitude of steady and dynamic patterns can form from random initial conditions.
+**What phenomena of physical interest are captured in the data:** Pattern formation: by sweeping the two parameters $f$ and $k$, a multitude of steady and dynamic patterns can form from random initial conditions.
 
 **How to evaluate a new simulator operating in this space:** It would be impressive if a simulator—trained only on some of the patterns produced by a subset of the $(f, k)$ parameter space—could perform well on an unseen set of parameter values $(f, k)$ that produce fundamentally different patterns. Stability for steady-state patterns over long rollout times would also be impressive.
 

datasets/post_neutron_star_merger/README.md

@@ -145,7 +145,7 @@ Here we include, for completeness, a description of the different simulation par
 - `variables` list of names of primitive state vector.
 
 ## What is interesting and challenging about the data:
-**What phenomena of physical interest are catpured in the data:** The 2017 detection of the in-spiral and merger of two neutron stars
+**What phenomena of physical interest are captured in the data:** The 2017 detection of the in-spiral and merger of two neutron stars
 was a landmark discovery in astrophysics. Through a wealth of
 multi-messenger data, we now know that the merger of these
 ultracompact stellar remnants is a central engine of short gamma ray

datasets/rayleigh_taylor_instability/README.md

@@ -76,7 +76,7 @@ where $\mu$ is the mean and $\sigma$ is the standard deviation of the profile. F
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** In this dataset, there are three key aspects of physical interest. Firstly, impact of coherence on otherwise random initial conditions. Secondly, the effect of the shape of the initial energy spectrum on the structure of the flow. Finally, the transition from the Boussinesq to the non-Boussinesq regime where the mixing width transitions from symmetric to asymmetric growth.
+**What phenomena of physical interest are captured in the data:** In this dataset, there are three key aspects of physical interest. Firstly, impact of coherence on otherwise random initial conditions. Secondly, the effect of the shape of the initial energy spectrum on the structure of the flow. Finally, the transition from the Boussinesq to the non-Boussinesq regime where the mixing width transitions from symmetric to asymmetric growth.
 
 **How to evaluate a new simulator operating in this space:**
 

datasets/supernova_explosion_128/README.md

@@ -71,7 +71,7 @@ Pressure (scalar field), density (scalar field), temperature(scalar field), velo
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:**
+**What phenomena of physical interest are captured in the data:**
 The simulations are designed as an supernova explosion, which is the explosion at the last moment of massive stars, in a high-density starforming molecular cloud with a large density contrast. An adiabatic compression of a monatomic ideal gas is assumed.
 To mimic the explosion, the huge thermal energy ($10^{51}$ erg) is injected at the center of the calculation box and going to make the blastwave, which sweeps out the ambient gas and shells called as supernova feedback. These interactions between supernovae and surrounding gas are interesting because stars are formed in dense and cold regions.
 

datasets/supernova_explosion_64/README.md

@@ -76,7 +76,7 @@ Pressure (scalar field), density (scalar field), temperature(scalar field), velo
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:**
+**What phenomena of physical interest are captured in the data:**
 The simulations are designed as an supernova explosion, which is the explosion at the last moment of massive stars, in a high-density starforming molecular cloud with a large density contrast. An adiabatic compression of a monatomic ideal gas is assumed.
 To mimic the explosion, the huge thermal energy ($10^{51}$ erg) is injected at the center of the calculation box and going to make the blastwave, which sweeps out the ambient gas and shells called as supernova feedback. These interactions between supernovae and surrounding gas are interesting because stars are formed in dense and cold regions.
 

datasets/turbulence_gravity_cooling/README.md

@@ -94,7 +94,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:**
+**What phenomena of physical interest are captured in the data:**
 Gravity, hydrodynamics and radiative cooling/heating are considered in the simulations. Radiative cooling/heating is parameterized with metallicity, which the ratio of heavier elements than helium. The larger and metallicity corresponds to the later and early stage of galaxies and universe, respectively.
 It also affects the time scale of cooling/heating and star formation rate. For instance, star formation happens at dense and cold region. With the strong cooling/heating rate, dense regions are quickly cooled down and generates new stars. Inversely, in the case of a weak cooling/heating, when gas is compressed, it is heated up and prevent new stars from being generated.
 

datasets/turbulent_radiative_layer_2D/README.md

@@ -64,7 +64,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:**
+**What phenomena of physical interest are captured in the data:**
 -	The mass flux from hot to cold phase.
 -	The turbulent velocities.
 -	Amount of mass per temperature bin (T = press/dens).

datasets/turbulent_radiative_layer_3D/README.md

@@ -63,7 +63,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** Capte the mass flux from hot to cold phase. Capture turbulent velocities. Capture the amount of mass per temperature bin ($T = \frac{P}{\rho}$).
+**What phenomena of physical interest are captured in the data:** Capte the mass flux from hot to cold phase. Capture turbulent velocities. Capture the amount of mass per temperature bin ($T = \frac{P}{\rho}$).
 
 **How to evaluate a new simulator operating in this space:** Check whether the above physical phenomena are captured by the algorithm.
 

datasets/viscoelastic_instability/README.md

@@ -81,7 +81,7 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
 
 ## What is interesting and challenging about the data:
 
-**What phenomena of physical interest are catpured in the data:** The phenomena of interest in the data is: (i) chaotic dynamics in viscoelastic flows in EIT and CAR. Also note that they are separate states. (ii) multistability for the same set of parameters, the flow has four different behaviours depending on the initial conditions.
+**What phenomena of physical interest are captured in the data:** The phenomena of interest in the data is: (i) chaotic dynamics in viscoelastic flows in EIT and CAR. Also note that they are separate states. (ii) multistability for the same set of parameters, the flow has four different behaviours depending on the initial conditions.
 
 **How to evaluate a new simulator operating in this space:**
 A new simulator would need to capture EIT/CAR adequately for a physically relevant parameter range.