Inviscid AI Brings Real-Time Physics Simulations to Data Centers and Industrial Facilities
The boom in artificial intelligence has produced a quieter, more stubborn problem beneath the headlines: data centers and industrial facilities are becoming some of the most power- and thermally-constrained buildings on Earth. Traditional building management systems, designed decades ago for predictable office loads, simply cannot keep up. Inviscid AI, a Y Combinator–backed startup, is rethinking AI data center cooling optimization from first principles — combining computational fluid dynamics with real-time IoT data to change how large facilities actually breathe and cool.
Inviscid AI builds physics-informed AI solutions that transform how buildings and data centers operate. The company’s platform ingests real-time IoT sensor data and feeds it into computational fluid dynamics (CFD) models to create live digital twins of each facility. Those twins simulate building performance continuously and autonomously optimize operations – adjusting airflow patterns, ventilation strategies, and cooling setpoints to eliminate dead zones, improve air distribution, and reduce the load on mechanical systems. The practical result for operators is lower HVAC power consumption, reduced cooling costs, and improved operational resilience, all while maintaining the tight environmental specifications that modern computing equipment demands.
The company was founded in 2025 by Kabir Jain and Ziming Qiu, a two-person team with complementary expertise at the intersection of physics and machine learning. Jain’s own bio reads simply, “I like physics and AI,” while Qiu describes himself as someone who “makes simulations” – an understated introduction to a technical founding pair tackling one of the hardest simulation challenges in the built environment. Turning CFD from a slow, offline engineering exercise into a fast, closed-loop control system is non-trivial, and it is exactly the kind of problem where deep physics understanding plus modern ML infrastructure can produce step-function gains.
The market opportunity for Inviscid AI is large and accelerating. Hyperscalers, colocation providers, and increasingly enterprises running on-premise AI clusters are under pressure to cool ever-denser racks while reducing energy consumption and meeting sustainability targets. Industrial facilities – manufacturing plants, warehouses, pharmaceutical environments – face similar challenges around airflow management, contamination control, and energy cost. A platform that can continuously optimize these environments without replacing the underlying mechanical systems has the potential to become a high-ROI retrofit for some of the most capital-intensive buildings in the world.
Inviscid AI is part of the Y Combinator Winter 2026 batch. Its positioning – physics-informed AI rather than generic machine learning – signals a level of technical rigor that is important for the types of customers it is pursuing. Data center operators and industrial engineers rightly distrust black-box models that cannot be inspected or verified; they want tools that respect the underlying physics of their facilities. By anchoring its AI data center cooling optimization platform in CFD and explicit physical models, Inviscid AI is positioning itself to win trust in an industry where getting cooling wrong can cost millions in downtime.