SimScale Emphasizes AI-Driven Simulation Workflow Automation

A LinkedIn post from SimScale describes a presentation at the NAFEMS U.K. conference by Alex Graham, focusing on bottlenecks in engineering simulation workflows. The post suggests that while cloud high performance computing and GPUs have reduced solve times, lead time driven by manual CAD handoffs, meshing, and setup remains a key constraint on R&D productivity.

According to the post, Graham framed “Agentic Engineering” as a way to address these issues by combining two elements: Physics AI, using deep learning surrogates to predict complex physics quickly, and Engineering AI, with agents that can interpret CAD geometry and automate repetitive setup tasks. This approach is positioned as a means to reduce manual intervention in simulation pipelines.

The post highlights a case study with Convion, a subsidiary of HD Hydrogen, where a Physics AI surrogate was reportedly trained to optimize a 600°C fluidic device. The AI was said to achieve results within 5% of high fidelity CFD while enabling broader design space exploration that reduced the component’s physical volume by about 50%.

The same case study, as described, indicates that re optimization under new boundary conditions could be completed in under an hour instead of taking weeks. For investors, this suggests SimScale is emphasizing AI driven efficiencies that could improve customer R&D velocity and potentially increase demand for its simulation platform by shortening development cycles.

The post also implies a shift in the role of engineers from software operators to higher level system architects defining requirements. If customers adopt such AI assisted workflows at scale, SimScale could strengthen its competitive positioning in cloud based CAE and simulation markets, potentially enhancing long term recurring revenue prospects and differentiation against traditional on premises tools.