CAMBRIDGE, Mass., April 30, 2026 /PRNewswire/ -- Today, JuliaHub announces the launch of 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 marks a fundamental shift in how physical systems are designed and built, bringing 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. Several Fortune 100 companies are already leveraging and Julia across several industrial sectors such as , , , , and .
Physical engineering represents one of the largest sectors yet to fully benefit from the AI revolution. While tools like Claude Code, Codex, and Gemini have transformed software development, industrial engineers have remained constrained by legacy tools. that a cumulative $106 trillion in investment will be necessary through 2040 to meet the need for new and updated infrastructure. The engineers planning and building these updates need a solution that allows them to move at the pace of AI-enhanced software. That's where Dyad comes in.
Dyad gives engineering teams : think Claude Code for the physical world. Dyad 3.0 launches today and builds on Dyad 1.0, which launched in June 2025, and Dyad 2.0, which launched in December 2025. Dyad connects autonomous agents with scalable physics simulations, rigorous controls, safety analysis, and the ability to generate code for embedded systems to bridge the gap between software and the real world. Whether it's a wastewater facility or an automobile, a scientific PhD is no longer required to develop highly detailed digital twins, tweak controllers for specialized deployment scenarios, and iterate on hardware designs to build the most efficient machine right the first time.
Dyad's cloud-based agents are designed to continuously 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 () 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. means engineers do not have to write every line of code in order to try millions of designs while giving engineers the right tools to make sure planes stay in the sky.
General-purpose AI cannot guarantee that a model obeys the laws of physics. In physical engineering, an error is not a bug to be patched; it's a bridge collapse or a battery fire. This has been the barrier blocking AI from playing a meaningful role in hardware engineering, until now. In for chemical process modeling, general LLM systems such as Codex, Claude Code (Opus), and Gemini barely completed the initial setup. Dyad almost entirely automated the whole process of creating model-predictive controllers to optimize yields of a chemical plant, a task that would typically take weeks.
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. This produces physically valid models that engineers can trust. For instance, in partnership with Binnies, a company with a 100-year heritage in water management, and Williams Grand Prix Technologies, that uses just four sensor inputs to predict pump faults in water distribution systems with over 90% accuracy.
Dyad 3.0 will be at a live event next month on May 19. to see live product demonstrations and hear from our customers on how they use Dyad across industries ranging from Aerospace to HVAC to utilities to Robotics.
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JuliaHub is a leader in Scientific AI, and its mission is to empower those tackling the world's toughest scientific and technical challenges with cutting-edge AI-first tools in a seamless, secure environment. The company was founded in 2015 by the creators of Julia, the high-performance open-source language developed at MIT and now used by over a million developers worldwide. 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.
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