SPlisHSPlasH Available Versions:
version 2.13.0
Available Versions:
version 2.13.0
SPlisHSPlasH is an open-source library for the simulation of fluids and solids using the Smoothed Particle Hydrodynamics (SPH) method. It efficiently models complex fluid behaviour by considering neighbouring particles. The library includes pressure solvers for the simulation of incompressible fluids and provides different methods for the simulation of viscosity, surface tension, vorticity, elasticity and drag forces.
With Inductiva, you can speed up your SPlisHSPlasH simulations by sending them to Cloud machines with hundreds of cores and terabytes of disk space.
How to Run SPlisHSPlasH on the Cloud
Running your SPlisHSPlasH simulations on the Cloud is easy. All you need is to create a short Python script that points Inductiva to the simulation artifacts you have on your computer, and we will take it from there.
On the right, we show how to use the Inductiva API to send a SPlisHSPlasH simulation to a 180 vCPU machine (c3d-standard-180) hosted on Google Cloud (GCP).
You can copy paste this Python script, adapt it to your own case. Your simulation will start right away, without waiting in a queue.
"""SPlisHSPlasH example."""
import inductiva
# Allocate Google cloud machine
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c3d-highcpu-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 22 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
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GX
NWChem
OpenFAST
OpenFOAM (ESI)
OpenFOAM (Foundation)
OpenSees
Quantum ESPRESSO
REEF3D
SCHISM
SNL-SWAN
SPlisHSPlasH
SWAN
SWASH
XBeach
