SimScale partners with AI Engineering to unlock meshless SPH simulation in the Cloud

SimScale, an AI-native cloud engineering simulation platform, has formed a strategic collaboration with AI Engineering to integrate the Pamics solver into the SimScale ecosystem

Utilising accelerated computing on Nvidia AI infrastructure, the integration removes meshing bottlenecks and reduces simulation runtimes for complex industrial applications that have traditionally struggled with grid-based methods, delivering 10-20x faster simulation speeds.

By combining AI Engineering’s Smoothed Particle Hydrodynamics (SPH) solver with SimScale’s cloud-native infrastructure, this partnership aims to democratise access to high-fidelity, meshless Computational Fluid Dynamics (CFD).

This performance leap helps to position SimScale as a high-velocity source of synthetic physics data, allowing teams to generate the scale and fidelity required for Physics AI model training and predictive Digital Twins. By combining cloud-native simulation, centralised data management, and accelerated computing, SimScale can help lay the foundation for training the next generation of AI models. This foundation supports downstream Physics AI workflows across the Nvidia ecosystem, including physics-informed models built in SimScale with Nvidia PhysicsNeMo.

This integration supports advanced visualisation workflows across the Nvidia ecosystem , including compatibility with applications built on Nvidia Omniverse libraries to provide photorealistic, physically-based rendering and immersive review of simulation results. This enables engineering teams to visualise, communicate, and validate complex fluid behaviour within broader Digital Twin workflows.

Created to handle complex, dynamic fluid behaviours that are difficult to capture with traditional simulation methods,  the Pamics solver uses a Lagrangian SPH approach, enabling engineers to simulate fluid dynamics from raw CAD geometries without meshing. This helps to accelerate simulation workflows for complex multiphase and free-surface flows, especially in scenarios involving arbitrary motion, fluid-structure interaction, and splashing, which are difficult to model with traditional grid-based methods.

Critical use cases include:

• Accurately predicting oil lubrication and cooling in complex gearboxes and high-power electric motors without simplifying geometry.
• Modelling multiphase flows, surface tension effects, and non-Newtonian fluids in mixers, agitators, and food processing equipment.
• Simulating vehicle wading, soiling, and contamination management for consumer, off-highway and industrial vehicles.

David Heiny, CEO of SimScale, said, “At SimScale, our mission is to empower engineers to explore thousands of engineering decisions in seconds. By integrating AI Engineering’s sophisticated Pamics solver, we are bringing a true ‘no-mesh’ workflow to the cloud, removing one of the biggest bottlenecks when it comes to simulating complex fluid dynamics with moving assemblies. Combined with accelerated computing on GPUs and Physics AI workflows, this enables our customers to build their own synthetic data engines, accelerating their path to predictive Digital Twins.”

Dr -ing. habil. Stefan Adami, CEO of AI Engineering, said, “We developed Pamics to handle the most demanding fluid dynamics challenges where traditional methods fail,  specifically where complex motion and free surfaces interact. Joining forces with SimScale allows us to scale this technology globally. As a member of Nvidia Inception, we have optimised Pamics to extract maximum performance from Nvidia GPUs. Delivering this through SimScale’s browser-based platform means engineers in the industrial and manufacturing sectors will be able to access high-end SPH capabilities instantly, without investing in expensive local hardware."