SPlisHSPlasH Available Versions:
version 2.13.0
Available Versions:
version 2.13.0
SPlisHSPlasH is an open-source Smoothed-Particle Hydrodynamics (SPH) simulator used for physically-based simulations of fluids and solids. It efficiently computes fluid particle interactions using a meshless Lagrangian approach, making it ideal for simulating complex fluid dynamics.
It’s typically configured via a single .json file that contains all the necessary information for the simulation, such as the geometry, physical properties of the fluid, boundary conditions, numerical parameters, and output files. In some cases, extra geometry files may also be used for more complex setups.
Give it a Go: Try SPlisHSPlasH on the Cloud with Inductiva!
This code example shows how to run a SPlisHSPlasH simulation on a c3d-standard-180 Google Cloud machine using our API. To try it out, simply paste the code into your Python environment.
Ready to test SPlisHSPlasH? Explore our Fluid Cube benchmarks, ranging from the default configuration (Fluid Cube S) to variations with smaller particle radii (Fluid Cube L and M). Each benchmark offers a practical case for evaluating the simulator’s performance and handling different fluid dynamics setups.
For more details on the simulator’s features and configurations, visit the official SPlisHSPlasH site.
"""SPlisHSPlasH example."""
import inductiva
# Allocate Google cloud machine
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c3d-standard-180")
# Initialize the Simulator
splishsplash = inductiva.simulators.SplishSplash()
# RRun simulation with config files in the input directory
task = splishsplash.run( \
input_dir="/path/to/my/splishsplash/files",
sim_config_filename="my_config_file.json",
on=cloud_machine)
# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()
task.download_outputs()
Dive Deep
Tutorial Series: Generate Synthetic Data for Physics-ML with SPlisHSPlasH
In this tutorial series, we’ll walk you through our approach to generating synthetic data at scale using the Inductiva API, designed for training Physics-ML models. We break down each step, providing practical insights based on an example from a published study.
Using the SPlisHSPlasH simulator, we’ll demonstrate how to set up and run fluid simulations while exploring the impact of hyperparameters on simulation fidelity and computational cost. This series is perfect for machine learning engineers and enthusiasts eager to dive into the realm of Physics-ML.
We’ve got 20 simulators ready for you to explore.
Just one click away from running your favorite open-source simulators on the cloud and at scale!
Why not give it a try? Explore our example codes and discover everything our API can offer.
AMR-Wind
CaNS
CP2K
DualSPHysics
FVCOM
FDS
GROMACS
GX
NWChem
OpenFAST
OpenFOAM (ESI)
OpenFOAM (Foundation)
Quantum ESPRESSO
Reef3D
SCHISM
SNL-SWAN
SPlisHSPlasH
SWAN
SWASH
XBeach
