Simscale

Engineering teams switch to cloud-native tools for what it gets them: more speed, running simulations and studies in parallel instead of queuing for a workstation or cluster, and broader access with more flexibility across their organizations. Those have been the reasons for years. But now there is another, and it is quickly becoming the more urgent: AI. AI is starting to touch nearly every step of a simulation project.

Robotics teams are under the same pressure as everyone else building hardware right now: less time to prove a design works. Automotive suppliers that used to have months to respond to an RFP now have weeks, medical device makers race for first-to-market approval, and every legged, wheeled, or 3D-printing robot still has to pass its compliance tests before it ships.

Undersize a pump and the top floor runs dry. Oversize it and you pay for that mistake on every electricity bill for the next 20 years. Both come down to one number most engineers estimate too late: major head loss, the pressure a fluid burns off to friction as it travels a straight run of pipe or duct. You calculate it with the Darcy-Weisbach equation, \(h_f = f \times \frac{L}{D} \times \frac{v^2}{2g}\), where the friction factor \(f\) comes from the flow regime and the pipe’s roughness.

Undersize a pump and the top floor runs dry. Oversize it and you pay for that mistake on every electricity bill for the next 20 years. Both come down to one number most engineers estimate too late: major head loss, the pressure a fluid burns off to friction as it travels a straight run of pipe or duct.

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At SimScale, our mission is to empower engineering teams everywhere to innovate faster by exploring thousands of engineering decisions in seconds. That means shipping capabilities that cut simulation lead time, remove setup friction, and expand the physics you can access from a single browser tab. This summer, the updates center on Engineering AI, the PAMICS® SPH solver integration and important electromagnetics capabilities for modeling motion.

What happens when a design review agent and a simulation agent can actually coordinate across platforms? That’s what Jon Wilde, VP of Product at SimScale, and Jeremy Andrews, Co-Founder and CTO of CoLab, demonstrated live at Design & Simulation Week 2026, hosted by engineering.com.

Every simulation platform has an invisible ceiling. It’s not the physics solver. It’s not the mesh quality or the HPC compute behind it. It’s the simple fact that every tool you work with was designed to be operated by a human, one step at a time, inside a single closed application. The friction in modern simulation workflows isn’t solve time. It’s everything around it. Logging KPIs to a PLM system. Triggering a downstream optimization study based on what the last run found.

The servers keeping global AI workloads running generate more heat per rack than most facilities were built to handle. A modern GPU cluster can push 50 kW per rack, ten times the load of a standard compute server from a decade ago. Cooling infrastructure that was adequate for traditional IT simply cannot scale to meet that demand, and the cost of getting it wrong is severe: thermal throttling, hardware failure, downtime, and in the worst cases, data loss at the moment of peak demand.

What happens when you replace the simulation bottleneck with an AI that thinks like your best engineer? That’s the question at the heart of SimScale’s latest webinar, featuring Jon Wilde, VP of Product at SimScale, and Armin Narimanzadeh, Manager of Thermofluids & Simulations at Convion.