Superpose provides a platform that integrates differentiable physics simulators directly into machine‑learning workflows, allowing AI models to obey conservation laws, material properties, and other physical constraints. By offering pre‑built physics modules, cloud‑based simulation resources, and APIs for Python, TensorFlow, and PyTorch, it enables researchers and engineers to train more accurate, data‑efficient models with built‑in physical realism.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and researchers often struggle to incorporate accurate physical laws into AI models, leading to predictions that violate real-world constraints and require costly post-processing.
Solution
Superpose creates a platform that embeds fundamental physics directly into machine‑learning workflows. By combining differentiable physics simulators with modern AI architectures, the system ensures that model outputs respect conservation laws, material properties, and other domain constraints. Users can train models on limited data while leveraging simulated physical priors, improving both accuracy and generalization. The platform provides APIs and tooling to integrate physics‑aware components into existing data pipelines, enabling rapid prototyping of scientifically grounded AI solutions.
Target Audience
Primary customers are AI researchers, data scientists, and engineering teams in sectors such as aerospace, automotive, and materials science who need to embed physical realism into predictive models.
Features
- Differentiable physics engines that can be coupled with neural networks for end‑to‑end training
- Library of pre‑built physical modules (e.g., fluid dynamics, rigid body mechanics, thermodynamics) customizable via code or visual interface
- Automatic enforcement of conservation laws and boundary conditions during model inference
- Scalable cloud‑based simulation backend with on‑demand compute resources
- API and SDKs for Python, TensorFlow, and PyTorch to embed physics‑aware layers into any model