Hidenn provides a unified simulation platform that lets engineers run high‑fidelity finite element analyses without manual mesh refinement, using automated pre‑processing and a proprietary universal element method. The platform also offers both data‑driven and physics‑only surrogate models, guided by large language model workflows, to accelerate design iterations and reduce computational cost. By integrating these capabilities, Hidenn streamlines the entire engineering simulation process from model setup to result interpretation.
Funding
Funding not disclosed
Founders
Product
Problem
Engineers often spend extensive time on manual pre‑processing and iterative mesh refinement to achieve high‑fidelity finite element analysis, which limits productivity and raises the cost of simulation for many design projects.
Solution
Hidenn provides an AI‑infused simulation platform that integrates large language model (LLM) guided workflows with automated finite element analysis (FEA) and surrogate modeling. The system automates model preparation and applies a universal element approach to deliver high‑quality solutions without the need for iterative mesh refinement. Users can also generate surrogate models that are either data‑driven, learning from prior simulations, or physics‑only, relying solely on underlying equations. By combining AI assistance with traditional computational mechanics, Hidenn accelerates the simulation process, reduces computational expense, and makes high‑fidelity analysis more accessible to a broader range of engineers.
Target Audience
Primary customers are mechanical, aerospace, and civil engineering teams in product design, manufacturing, and research organizations that require high‑accuracy simulation but seek faster, more cost‑effective workflows.
Features
- LLM‑driven workflow interface that guides users through model setup and boundary condition definition
- Automated pre‑processing that creates simulation‑ready meshes using a proprietary universal element methodology
- High‑fidelity FEA solver that delivers accurate results without iterative mesh refinement
- Data‑driven surrogate modeling that learns from previous simulation data to provide rapid approximations
- Physics‑only surrogate modeling that generates fast predictions based purely on governing equations
- Cloud‑enabled execution environment for scalable compute resources and collaborative access