Trusted AI for the physical world
Modern industry runs on systems that cannot be fully observed, fully modeled, or fully controlled. When those systems fail, or simply underperform, the consequences are measured in safety, cost, and national security. Pattern-matching AI wasn't built for that. You can't train your way to an answer that history has never seen.
Geminus exists to change that.
We build physics-native AI that understands, predicts, and optimizes complex industrial systems in real time. Our models are trained on a combination of physics-based simulation and historical data. This gives them the ability to reason accurately about conditions that have never been observed before.
That capacity, to go beyond the boundaries of observed history, is what we've built at Geminus. We wrap those models in uncertainty-aware intelligence and compose them into multi-system configurations that capture how real-world infrastructure actually behaves: as interconnected, dynamic systems under constant pressure.
From computational science to high consequence systems
At the University of Michigan’s Institute for Computational Discovery & Engineering, Dr. Karthik Duraisamy and Dr. Alex Gorodetsky advanced physics-based modeling, uncertainty quantification, and scientific machine learning for systems that cannot be directly observed or tested. That work became the foundation for Geminus.
Deep learning gave computational scientists a new instrument, combining with or training on physics-based simulations to run orders of magnitude faster without specialized infrastructure. Powerful, but limited. These models remained idealized and unable to reason or act across complex, uncertain systems.
The central challenge in deploying AI on physical systems is the sim-to-real gap. Models trained in simulation fail under real-world conditions. Geminus addresses this by combining physics-based simulation with real-world data, keeping models grounded as systems evolve.
The second challenge is uncertainty. Industrial systems are never fully observed. Sensors fail, data is sparse, and conditions drift. Most AI ignores what it does not know. In high-consequence systems, that is a liability. Geminus embeds uncertainty-aware reasoning grounded in US DOE and DoD science, enabling systems to act with awareness of both knowns and unknowns.
These models are composed across systems, capturing how infrastructure behaves as interconnected networks where interactions define performance, risk, and failure.
Geminus is not experimental.
Together with leading energy, industrial, space, and defense institutions, our systems are deployed across complex infrastructure, delivering measurable improvements in performance.
We are building the system that closes the last mile between digital intelligence and the physical world. This is where complexity and real-world consequences converge; continuously understanding, optimizing, and improving the mission-critical infrastructure that powers the global economy.
Who we are
CEO
Built an industry around simulating the physical world.
Scaled Fluent through its acquisition by ANSYS (acquired by Synopsys for $35B in 2025). As VP of Product at Autodesk, he drove the introduction of the first generative design tool, an AI offering that co-designs alongside an engineer.
Chief Scientist
Global pioneer in Scientific Machine Learning. Architect of the Scientific Foundation Model.
Arthur B. Modine Professor at the University of Michigan. Director of MICDE. Leads a $1B+ national initiative with Los Alamos to institutionalize US leadership in physics and scientific AI. Authority for DARPA, NASA, DOE.
Chief AI Architect · Geminus Labs
Authority on autonomous decision-making under uncertainty.
Associate Professor of Aerospace Engineering, University of Michigan. PhD MIT. Former John von Neumann Fellow at Sandia National Labs. Moved physics-native AI from static predictions to real-time autonomous actions.
VP of Product
20+ years in physics-based simulation, digital twins, and industrial IoT.
Product Management at ANSYS, drove strategy for digital twins, reduced-order modeling, and simulation interoperability. At Synopsys, built and scaled physical modeling across semi, aerospace, automotive, and controls.
GM, Energy Vertical
Three decades deploying transformational digital across the global energy value chain.
26-year career at Schlumberger (SLB), serving on the Corporate Strategy Team. Led Global Partnerships and Alliances. Pioneered strategies to integrate digital capabilities into the legacy energy sector.
Chief Operating Officer
23-year track record scaling digital industrial businesses at the intersection of AI and energy.
Decade at Schlumberger (SLB), pioneered the industry’s first automated AI systems for SAGD and North Sea assets. SVP of Digital Consulting at Worley, grew global digital business 4× in 36 months.
Geminus helps people operate and optimize complex infrastructure with precision, and confidence under real-world conditions.
We are working toward a future where every critical system can be understood, trusted, and continuously improved.
We believe progress comes from understanding how the world actually works.