We are excited to introduce eigenspacedesign, a Physics AI simulation platform built to help engineers run, manage, and scale engineering simulations more efficiently.

The first version of eigenspacedesign focuses on creating a complete simulation workflow, starting from mesh import and simulation setup to solver execution and post-processing. The platform is being developed to support modern engineering teams that need faster design exploration, better simulation data management, and AI-assisted physics workflows.

What this release introduces

This initial release lays the foundation for:

• Mesh import and model setup

• Load and boundary condition definition

• Linear static simulation workflow

• Solver execution inside the platform

• Basic post-processing and result visualization

• Simulation data organization for future AI and ML workflows

Why we are building this

Traditional simulation workflows are often fragmented across multiple tools, manual steps, and disconnected data sources. eigenspacedesign aims to bring the complete simulation process into a unified environment where engineers can move from setup to results faster.

Our long-term vision is to turn simulation data into reusable Physics AI models, enabling faster predictions, smarter design decisions, and scalable engineering intelligence.

What comes next

In upcoming updates, we will continue improving the core simulation workflow and add more capabilities for post-processing, parametric studies, automation, collaboration, and AI-driven simulation acceleration.

This is the first step toward building a Physics AI platform for next-generation engineering simulation.