We are happy to announce a launch of Siml.ai alpha release to the public!

Siml.ai is a software platform for AI-based numerical simulation. It provides a visual editor for building high-performance physics simulators that leverage deep learning techniques (also called surrogate models). Additionally, the platform features tools for training these simulator models in powerful GPU-based cloud servers and easy-to-use AI inferencing and 3D visualization pipelines. There's a paradigm shift happening in the world of physics simulation. Deep learning methods are outperforming even the most optimized classical numerical solvers. The key components of these methods are physics-informed neural networks (PINNs), variations of Fourier neural operators (FNOs) and deep operator networks (DeepONets). These approaches also completely eliminate the need for meshing the geometries.

In complex numerical simulations, it’s important to precisely quantify the underlying physical mechanisms in order to analyze them. High-dimensional scientific simulations are computationally very expensive to run, and solvers and parameters must often be tuned individually to each system studied. 

Machine learning approaches tend to considerably decrease the load on computational resources by reducing the dimensionality of the studied problem and by factoring in the fact that AI-based simulators need to be trained only once while actual numerical simulation is computed during the inference of the model. This, compared to computing physics every time a simulation is re-run, brings a large efficiency benefits together with shortened time for the actual computations as trained models.

Siml.ai platform provides a set of tools to easily create, train and deploy powerful physics-informed neural network models with complex physics and custom partial differential equations. Building an AI-based simulator is extremely easy in our Siml.ai Model Engineer.

Siml.ai Model Engineer is a tool for visually creating and optimizing general-purpose "learnable simulators" (e.g. for simulating fluid flows, multiphase flows, mechanical deformation, solid-fluid interaction and so on) using deep learning techniques. Our aim is to make the process of building such simulators simple, visual and understandable.

Within the Siml.ai platform, Model Engineer has a powerful position of being a funnel through which we plan to build a large database of trained models by individual engineers, researchers or companies. This will allow us to create a new market for ready-to-use, "packaged" AI simulators – essentially a marketplace in which these entities can earn money from simulators trained by them by allowing other users to use them in their projects through Simulation Studio.

Simulation Studio is a hybrid between web-based and native application for solving engineering and scientific problems leveraging pre-trained and optimized models of AI-based physics simulators. Since the simulations are computed by inferring these models, the time it takes to compute one timestep even of highly irregular simulation domain is in low tens of milliseconds, resulting in real-time simulation and visualization of physical phenomena. High-fidelity visualization rendering is achieved by leveraging the powerful Unreal Engine under the hood.

Simulation Studio provides a 3D interface for high-fidelity, interactive in-situ visualizations of running numerical simulations.

With this alpha release, we're already extremely excited about the next phases of Siml.ai and what new possibilities will be unlocked because of it.

If you want see what is planned for the future, make sure to check out our public roadmap!

We're also giving out 100% discount for 1 month for everybody who completes our Siml.ai Early Adopter Survey - you can get more information in our community Discord, which you can join here.