The Team
Elias Farah, PhD
NumFlow is founded and led by Elias, a computational engineer holding a dual PhD in Computational Mechanics from Cranfield University in the UK and Université de technologie de Compiègne in France. His doctoral research centred on immersed boundary methods and fluid-structure coupling, developing custom open-source solvers in OpenFOAM from the ground up, enabling simulation of freely moving, rigid structures interacting with surrounding fluids, without the need for mesh deformation.
Beyond his PhD, his research background spans molecular dynamics simulation using Direct Simulation Monte Carlo method, and aerodynamic shape optimisation using genetic algorithms, giving NumFlow a broad and deep technical foundation across computational mechanics, fluid dynamics, optimisation, and scientific software.
Jean Rizk, PhD
Part of the NumFlow team, Jean is a mechanical engineer specialized in solid mechanics, holding a PhD in Numerical Mechanics and Materials Mechanics from the Université de Technologie de Compiègne in France. His doctoral research focused on developing a real-time wear monitoring system for sheet metal blanking tools used in the manufacturing industry, combining advanced material characterization, explicit and implicit finite element modeling, and machine learning classification models built on hybrid experimental and numerical datasets.
Beyond his doctoral work, he has developed extensive expertise in finite element analysis for complex engineering applications, material damage and fracture characterization, and the implementation of digital twin and artificial intelligence technologies in industrial environments. This multidisciplinary background provides NumFlow with a strong technical foundation spanning solid mechanics, structural analysis, advanced simulation, and AI-driven industrial solutions.
Founding Story
The expertise. When it matters.
NumFlow was founded on a precise observation that high-end simulation expertise, the kind that lives in research labs and leading engineering departments, is rarely accessible to deeptech companies, at the stage where it matters most.
Deeptech startups and engineering-driven scale-ups reach a stage where simulation stops being exploratory and starts being critical. Prototypes need to be justified before they are built. Technologies need to be validated before investors commit. Design decisions need to be traceable before industrial partners come on board. At that inflection point, the quality of the simulation work directly shapes the credibility of the product.
NumFlow was built to operate at exactly that stage. By combining research-grade methods with open-source tooling and AI-accelerated development workflows, we deliver R&D engineering expertise that allows engineering teams to move forward with confidence, faster and more efficiently than before. Costs reflect the work done, not software licensing fees.
Working Philosophy
Physics first. Always.
NumFlow approaches every project from first principles. The starting point is always the physics: understanding the problem deeply before choosing a method, and choosing a method before touching a tool. Solutions are built to be reproducible, documented, and deployable. Not black boxes. AI is no exception. We use it where it can accelerate delivery without compromising engineering integrity, deliberately and transparently.
From physics to validated results: high-end simulation engineering for the problems that matter.