Dr. Caglar Aytekin
Lead AI Developer
Foundational model capabilities scale with data and compute. While compute used to be the bottleneck, today the limiting factor is high-quality, diverse, and structured data, especially in domains outside of language and vision.
In large language models (LLMs), we have already begun to exhaust the available high-quality internet-scale text corpora.
The opportunity in physics simulations
Unlike text data, the world of physics simulations is still vastly under-explored. There is a huge demand for high-fidelity, multi-scale, multi-physics simulation data that is consistent, accurate, and diverse.
Creating a foundational model for physics capable of generalizing across domains like fluid dynamics, electromagnetics, structural mechanics, and thermal systems requires massive volumes of simulation data generated under controlled and consistent physical laws.
Where does Quanscient comes in?
Multiphysics simulation at scale
Quanscient specializes in high-performance, cloud-native multiphysics simulations. Quanscient Allsolve combines quantum-inspired algorithms, modern HPC, and efficient numerical solvers to simulate complex physics with speed and scalability that legacy tools can't match.
Synthetic data generation
Quanscient Allsolve’s ability to run large volumes of simulations in parallel enables the generation of variant-rich synthetic data. This data is grounded in physics equations, free from experimental noise, and ideal for training data-hungry foundational models.
Flexible physics engine
With support for electromagnetics, mechanics, fluid dynamics, and heat transfer, Quanscient Allsolve enables cross-domain simulation pipelines. This flexibility is essential for foundational models that must generalize across physics domains.
Integration with AI workflows
Quanscient is also investing in AI-augmented simulation workflows, making it possible to use neural operators or surrogate models. These approaches benefit from foundational training datasets created through Quanscient’s high-throughput simulations.
Toward a foundational model for physics
Just as LLMs were enabled by massive datasets scraped from the internet, a foundational model for physics will require billions of high-quality simulation data points. Quanscient Allsolve can act as a data engine, producing these points with rigorous control over boundary conditions, parameter ranges, and governing equations.
With this capability, Quanscient Allsolve isn’t just enabling simulations, it’s powering the next paradigm shift in physics-informed AI.
