Core Insights - SimScale has announced a strategic collaboration with AI Engineering GmbH to integrate the PAMICS® solver into its cloud engineering simulation platform, enhancing simulation speeds by 10-20 times for complex industrial applications [2][4]. Company Overview - SimScale is recognized as the world's first AI-native cloud platform for engineering simulation, serving over 800,000 users globally and enabling rapid exploration of design decisions [10]. - AI Engineering GmbH specializes in advanced simulation solutions, particularly in Smoothed Particle Hydrodynamics (SPH) and AI-enhanced engineering tools [11]. Technological Advancements - The integration of AI Engineering's PAMICS solver with SimScale's infrastructure aims to democratize access to high-fidelity, meshless Computational Fluid Dynamics (CFD) [3]. - The PAMICS solver utilizes a Lagrangian SPH approach, allowing for direct simulation of fluid dynamics from raw CAD geometries, thus eliminating the need for meshing [6]. - This collaboration supports advanced visualization workflows compatible with NVIDIA Omniverse libraries, facilitating photorealistic rendering and immersive review of simulation results [5]. Industry Applications - The PAMICS solver is designed to handle complex fluid dynamics scenarios, including multiphase flows, fluid-structure interactions, and arbitrary motion, which are challenging for traditional methods [6][9]. - Key use cases include accurately predicting oil lubrication in gearboxes, modeling multiphase flows in industrial mixers, and simulating vehicle wading and contamination management [9]. Strategic Goals - The partnership aims to empower engineers to explore thousands of engineering decisions rapidly, thereby accelerating the development of predictive Digital Twins [7]. - By leveraging NVIDIA's accelerated computing infrastructure, the integration is expected to enhance the performance of simulation workflows significantly [4][7].

my journey started uh largely instrumenting code to run on a single GPU. This was state-of-the-art uh when I started and that that was sort of great. We had some challenges but we had this this disc media called SATA and it sort of kept up with what we wanted to do but it didn't take long when we started instrumenting as science instrumenting code for multiGPU particularly as science began to increase the size of data we could do more.So they were doing more. So we were growing the platform capability by ad ...