diff --git a/datasets/MHD_256/README.md b/datasets/MHD_256/README.md index 315f4f6f..f0e8b53b 100755 --- a/datasets/MHD_256/README.md +++ b/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. diff --git a/datasets/MHD_64/README.md b/datasets/MHD_64/README.md index 1670e8ec..62b09eaa 100755 --- a/datasets/MHD_64/README.md +++ b/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. diff --git a/datasets/active_matter/README.md b/datasets/active_matter/README.md index 03cdfc87..99ce8614 100755 --- a/datasets/active_matter/README.md +++ b/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. diff --git a/datasets/euler_multi_quadrants_openBC/README.md b/datasets/euler_multi_quadrants_openBC/README.md index dd71996e..71339b4c 100755 --- a/datasets/euler_multi_quadrants_openBC/README.md +++ b/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. diff --git a/datasets/euler_multi_quadrants_periodicBC/README.md b/datasets/euler_multi_quadrants_periodicBC/README.md index 49ae5205..32731489 100755 --- a/datasets/euler_multi_quadrants_periodicBC/README.md +++ b/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. diff --git a/datasets/gray_scott_reaction_diffusion/README.md b/datasets/gray_scott_reaction_diffusion/README.md index 1ddc0f6d..c3634629 100755 --- a/datasets/gray_scott_reaction_diffusion/README.md +++ b/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. diff --git a/datasets/post_neutron_star_merger/README.md b/datasets/post_neutron_star_merger/README.md index 80fd79f3..0a3e50f9 100755 --- a/datasets/post_neutron_star_merger/README.md +++ b/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 diff --git a/datasets/rayleigh_taylor_instability/README.md b/datasets/rayleigh_taylor_instability/README.md index b1eb3f29..d1c7d5b9 100755 --- a/datasets/rayleigh_taylor_instability/README.md +++ b/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:** diff --git a/datasets/supernova_explosion_128/README.md b/datasets/supernova_explosion_128/README.md index 097bd1dd..a00c97f4 100755 --- a/datasets/supernova_explosion_128/README.md +++ b/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. diff --git a/datasets/supernova_explosion_64/README.md b/datasets/supernova_explosion_64/README.md index baefd4d6..8204aef9 100755 --- a/datasets/supernova_explosion_64/README.md +++ b/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. diff --git a/datasets/turbulence_gravity_cooling/README.md b/datasets/turbulence_gravity_cooling/README.md index 8f8d755a..4a2134b6 100755 --- a/datasets/turbulence_gravity_cooling/README.md +++ b/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. diff --git a/datasets/turbulent_radiative_layer_2D/README.md b/datasets/turbulent_radiative_layer_2D/README.md index 66400c16..f9ed75d7 100755 --- a/datasets/turbulent_radiative_layer_2D/README.md +++ b/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). diff --git a/datasets/turbulent_radiative_layer_3D/README.md b/datasets/turbulent_radiative_layer_3D/README.md index fcb1101b..f5a5027e 100755 --- a/datasets/turbulent_radiative_layer_3D/README.md +++ b/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. diff --git a/datasets/viscoelastic_instability/README.md b/datasets/viscoelastic_instability/README.md index 5124e3a4..604dbd6e 100755 --- a/datasets/viscoelastic_instability/README.md +++ b/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.