The Case for a Next Gen CFD Platform

Eigen is building a computational fluid dynamics platform intended to shorten the path from complex engineering questions to usable answers across energy transition, mobility, aerospace, and advanced manufacturing.

The core bottleneck

Many high-value engineering problems depend on CFD, but time-to-solution remains a constraint. Faster simulation can change how teams certify aircraft, validate catalysts, design bioreactors, model reservoirs, and test industrial systems before committing capital in the field.

Legacy CFD stacks were shaped by hardware limits that no longer define the frontier. To move faster, solvers need software that scales on emerging supercomputers and numerical methods designed for the hardware now being built around massive parallel compute.

Eigen's approach

The platform direction is to rebuild state-of-the-art solvers on top of AI execution platforms such as TensorFlow and PyTorch, using them as high-performance linear algebra engines. That foundation can load GPU kernels more effectively while giving Eigen room to implement newer numerical methods at speed.

For reservoir modeling and carbon capture storage workflows, the goal is order-of-magnitude acceleration over current approaches where runs can stretch over weeks. Compressing that cycle creates room for more design exploration, faster operational decisions, and stronger economics.

Why now

The same parallel compute breakthroughs that accelerated generative AI can be applied to numerical simulation. GPUs, AI accelerators, and increasingly accessible supercomputing resources create a new hardware baseline, but they also require new algorithms and solver architectures to use that compute well.

Energy transition applications

Houston's energy ecosystem needs faster engineering tools for transition work. Eigen's CFD direction is especially relevant where speed, accuracy, and design iteration determine whether a project can move from concept to deployment.

1. Carbon capture and storage systems, including subsurface flow and sequestration design.
2. Renewable fuel and bioreactor scale-up, along with wind and solar optimization.
3. Air emissions modeling, plume dispersion, and source identification.
4. Water treatment, flood control, irrigation, and distribution networks.
5. Electric vehicle battery cooling, aerodynamics, and efficiency analysis.
6. Sustainable infrastructure, including efficient buildings, HVAC, and urban systems.
7. Marine and ocean engineering for fluid-structure interaction and offshore systems.
8. Fusion plasma modeling and other demanding clean-energy simulation workloads.

Product journey

Eigen's first solver work targets complex turbulence modeling with a modern numerical method and a lean TensorFlow-based dependency model. The broader product path is to commercialize solver capability for customers with high-value industrial CFD requirements.