JuliaHub Launches Dyad 3.0

JuliaHub launches Dyad 3.0 and a $65M series B funding round led by Dorilton Capital, with participation from General Catalyst, AE Ventures, and technology investor and former Snowflake CEO Bob Muglia. Dyad brings autonomous AI agents into the digital design and testing of industrial machines.

From heat pumps to satellites to semiconductors, engineering teams can compress cycles of design, testing, and building from months to minutes in industrial sectors such as aerospace, government, automotive, HVAC, and utilities.

Hardware Innovation

Dyad gives engineering teams an AI-first environment to model, test and validate industrial systems. Dyad 3.0 launches and builds on Dyad 1.0, which launched in June 2025, and Dyad 2.0, launched in December 2025. Dyad connects autonomous agents with scalable physics simulations, controls, safety analysis, and ability to generate code for embedded systems. 

"It's not about helping engineers complete one small task at a time. It's agentic engineering at scale, where teams can feed a full specification to Dyad and have it design the complete system. Spec in. Design out," says Viral Shah, CEO of JuliaHub.

Digital Twins with Scientific Machine Learning

Dyad's cloud-based agents are designed to scan through the world's scientific knowledge to constantly improve models. AI-automated lab testing is growing to ensure models match physical reality. Streaming data mixed with Scientific Machine Learning (SciML) makes it possible for models to automatically grow as the system learns from the real world.

Dyad's simulation ecosystem and language offer a foundation on which all of these learnings are relayed back to engineers to check the processes, determine whether assumptions match customer requirements, and be the human in the loop that ensures the safety of the final product. 

Dyad to Implement AI for Science in Real World

Dyad's modeling language is purpose-built to be easy for AI agents to understand. Its foundational logic is grounded in the laws of physics, allowing its agents to reason about how fluids move through machines, how wind speed and temperature affect components, and how fundamental forces like gravity shape design. 

About JuliaHub

JuliaHub was founded in 2015 by creators of Julia, a high-performance open-source language developed at MIT. JuliaHub combines advanced mathematical computing and machine learning expertise to enable Scientific Machine Learning (SciML) techniques, Digital Twin solutions, and next-generation modeling and simulation in aerospace, automotive and other industrial verticals.

Sources: Press materials received from the company and additional information gleaned from the company’s website.