JuliaHub has announced the launch of Dyad 3.0, a major update to its AI-native systems simulation platform designed for the modeling, validation, and optimization of complex physical systems. The release introduces autonomous simulation agents that help engineering teams automate model generation, simulation execution, and physics-based validation workflows.
Dyad 3.0 is already being used by Fortune 100 organizations and was unveiled during a global livestream demonstration.
JuliaHub has unveiled Dyad 3.0, positioning the platform as an AI-native environment for complex engineering simulation and physical system validation. The latest release introduces autonomous simulation agents capable of working alongside engineers to automate time-intensive design and modeling workflows.
Dyad combines large language models, multi-physics simulation, Scientific Machine Learning (SciML), and enterprise deployment capabilities into a unified engineering platform. According to JuliaHub, the platform is designed to help organizations accelerate validated engineering workflows while maintaining physics-based rigor and safety controls.
"AI has transformed software development through agents that combine LLMs with open-source compilers, but engineering physical systems requires the combination of LLMs with a physics compiler that grounds hardware designs in physical laws," said Dr. Viral B. Shah, CEO and co-founder of JuliaHub. "Dyad 3.0 brings agentic AI directly into the engineering workflow by combining autonomous agents, a multi-physics compiler, high-fidelity simulation, SciML, and enterprise deployment capabilities into one seamless environment. It gives engineers the leverage of AI while preserving the rigor, safety, and verification that physical systems demand."
Dyad 3.0 is designed to help engineering teams working on aircraft, electric vehicles, semiconductors, HVAC systems, medical devices, utilities, and other industrial applications.
With the updated platform, engineers can provide requirements documents, previous design files, historical test data, and plain-language instructions. Dyad agents can then automatically generate candidate models, execute simulations, evaluate trade-offs, enforce physical and safety constraints, and produce validated control code for deployment.
JuliaHub emphasized that engineers remain responsible for reviewing decisions, setting design priorities, and approving final outputs, while Dyad automates repetitive simulation and optimization tasks.
The Dyad 3.0 release introduces several new platform capabilities aimed at accelerating AI-native engineering workflows.
Autonomous agents can interpret engineering requirements, propose design candidates, execute simulations, and refine models with human oversight.
The platform expands support for industrial digital twins and predictive maintenance applications powered by Scientific Machine Learning.
Dyad now includes expanded HVAC-focused modeling tools, refrigerant spline accuracy improvements, and templates for common system architectures.
Functional Mock-up Unit (FMU) interoperability improvements strengthen integration with broader engineering and simulation toolchains.
JuliaHub also introduced a preview of multibody dynamics capabilities supporting robotics, aerospace mechanisms, and vehicle dynamics applications.
The release includes enhancements for enterprise installation, lifecycle management, security, compliance, and distributed engineering environments.
JuliaHub noted that while AI adoption has accelerated across software development, engineering organizations managing physical systems face stricter safety, physics, and validation requirements.
The company says Dyad addresses this challenge by combining AI agents with validated simulation infrastructure capable of reasoning across requirements, operational data, simulation models, and prior engineering designs.
According to JuliaHub, the platform enables engineering teams to reduce manual model construction, shorten development cycles, accelerate time-to-market, and improve exploration of complex multi-physics scenarios.
JuliaHub showcased several enterprise and industrial use cases during the Dyad 3.0 launch event, including:
JuliaHub described Dyad 3.0 as part of a new category it calls “agentic simulation,” combining autonomous AI agents with physics-grounded engineering simulation.
The company argues that traditional software agents lack the validation frameworks needed for physical systems, while legacy simulation platforms were not designed around AI-native workflows and natural-language interaction models.
Dyad 3.0 is now available through JuliaHub for enterprise evaluations and production deployments.
About JuliaHub
JuliaHub, formerly Julia Computing, was founded in 2015 by the four co-creators of Julia - Dr. Viral Shah, Prof. Alan Edelman, Dr. Jeff Bezanson, and Stefan Karpinski - together with Deepak Vinchhi and Keno Fischer. Julia is a high-productivity language for scientific computing used by over 1,000,000 users, with more than 10,000 companies and more than 1,500 universities. Julia's creators have won the James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.
About Dyad
Dyad is an AI-native systems simulation product that accelerates hardware engineering in industrial verticals. Built on the Julia programming language, Dyad helps teams create validated, reliable models through agentic commands while enforcing physics at every step. Dyad includes built-in capabilities to bring data and machine learning into scientific models.
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