Bringing Scientific Machine Learning to Industrial Digital Twins with Dyad and Ansys TwinAI
Digital twins are evolving from simulation models into adaptive, continuously improving representations of real systems. This talk introduces Ansys TwinAI™, part of the Synopsys portfolio, and outlines how the integration of JuliaHub's Dyad brings Scientific Machine Learning (SciML), differentiable programming, and physics-informed artificial intelligence into digital twin workflows. We will highlight the value of hybrid digital twins and present representative engineering use cases.
Digital twins are increasingly expected to do more than replicate system behaviour—they must adapt to new data, improve over time, and remain trustworthy for engineering decision-making. Achieving this requires combining the predictive power of artificial intelligence with the robustness and explainability of physics-based models.
In this session, we will introduce Ansys, now part of Synopsys, and provide a brief overview of Ansys TwinAI™, artificial intelligence-powered digital twin software designed to support validation, operation, and deployment of digital twins in cloud environments. We will then discuss the recently announced collaboration between Synopsys and JuliaHub to integrate Dyad, JuliaHub's next-generation simulation platform, into TwinAI.
The core of the talk will focus on why bringing Julia and Dyad into the TwinAI ecosystem matters. Dyad combines component-based acausal modelling, automatic equation generation, Scientific Machine Learning (SciML), and differentiable programming, enabling the creation of hybrid digital twins that blend first-principles physics with data-driven learning. These capabilities create opportunities to improve model fidelity, accelerate model development, and continuously update digital twins as operational data becomes available.
The session will provide a high-level overview of representative use cases, including:
Adaptive asset monitoring and predictive maintenance, where physics-based models are enhanced using operational data to improve forecasting accuracy and fault detection.
Engineering system optimisation and calibration, where differentiable models and SciML techniques enable efficient tuning of digital twins against measured data while preserving physical consistency.
Attendees will gain an understanding of the strategic vision behind the TwinAI–Dyad integration, the role that Julia and SciML can play in next-generation digital twins, and how engineers can benefit from hybrid approaches that bridge simulation and real-world operation.
This talk is intended for both Julia users interested in industrial deployment of SciML and engineers exploring the future of AI-powered digital twins.
Edward Carman is a Lead Application Engineer at Ansys, part of Synopsys, specializing in engineering analysis and model-based approaches to product development, with a particular focus on packaging and deployment of physics-based simulation models into workflow automation, democratisation and digital twin applications.