From design to
post-processing in 4 clicks

Upload. Run simulations. Run post-processings. Download.
Any design your team wants to test turns into actionnable results in less than 10 minutes.
Book a demo

/ How it works?

Upload a geometry
Input your 3D geometry and choose boundary conditions
Run simulation
Choose which simulations you want to run regarding your conditions and run them with a single click
Get a result
In minutes, get your outputs : volume, surface, evolution curves, coefficients

/ Setup phase

Data
Design data from your past simulations and trials
Physics Eq
Concepts for solving Physics PDE
Extrality

/ Benefits

Faster time-to-market
More reliable numerical prototypes means hitting production with more confidence
Save engineers time for value-added tasks
Relief your team from the time-consuming hassles
Ease collaboration around product design
Easy collaboration between departments, designers, engineers, and business. Experts and non-experts can use the same tool
Leverage on your simulation investment
Get valuable insights today out of your past data
Reduce computation costs
Cut down expenses spent on CPU power
Test all your designs without constraints
Tolerant to imperfect geometry, no need to clean it up, sparing preparation time

/ Uses cases

In this study, we are using Deep Learning Physics (DLP) to build a generic AI-model for hydrodynamic studies of tankers.‍ First we will introduce the Deep Learning Physics technology, then we will describe the dataset used to build a tanker boat DLP-model and finally assess this model vs CFD.
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The objective of this study is to minimize the resistance of a ship at the target operational speed and to show that it can be done in a short time with few computing resources. First we will recall the context of the global shipping industry and a way to cut down emissions, then we will describe the procedure of hull form optimization and finally we will show how to effectively do it with Extrality Deep Learning Physics Platform.
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Need to hit production with short go-to-market and better control?
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