S2 Labs provides an AI‑native infrastructure that replaces traditional physics solvers with neural surrogates such as Fourier Neural Operators, DeepONets, and graph neural networks. By delivering simulation results up to 10,000× faster while preserving governing equations, the platform enables real‑time digital twins for design optimization, material discovery, and adaptive control across engineering and asset‑management workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering and scientific teams often rely on high-fidelity physics simulations that are computationally intensive, requiring days or weeks to run, which slows design iteration, optimization, and real-time decision making.
Solution
S2 Labs offers an AI-native infrastructure that replaces traditional solvers with neural surrogates such as Fourier Neural Operators, DeepONets, and graph neural networks. By training these models on physics-based data, the platform delivers simulation results up to 10,000× faster while preserving the underlying governing equations. The accelerated digital twins enable real-time design optimization, material discovery, and adaptive control across industries ranging from aerospace to construction. Results are delivered through a cloud-enabled workflow that integrates with existing engineering pipelines, allowing teams to iterate rapidly without sacrificing accuracy.
Target Audience
Primary customers are engineering firms, R&D departments, and asset‑management organizations that require high‑speed, physics‑accurate simulations for product design, material engineering, and operational control.
Features
- Neural operator models (FNOs, DeepONets, GNNs) that learn directly from PDE data for physics‑consistent predictions
- Pre‑trained surrogate libraries for topology optimization, material microstructure design, and offshore asset management
- Cloud‑native execution environment that scales inference to millions of simulations per day
- API and SDK integrations for seamless embedding into CAD, CAE, and IoT platforms
- Real‑time monitoring dashboards that visualize surrogate outputs and uncertainty metrics
- Support for custom physics extensions, enabling users to tailor models to specific domain equations