JuliaCon 2026

The Julia language is a proven technology for technical computing. So it is only natural for people to build engineering-related tools on top of it. In this workshop, we'll discuss our Dyad platform for system modeling and how this utilizes both Julia and ModelingToolkit to deliver Scientific Machine Learning (SciML) to engineers in industry.

The rapid spread of AI into all aspects of society has led to a corresponding surge in data centers to support the exploding computing demand. Data centers are complex interconnected physical systems with thermal power generation, electrical power conversion, and cooling systems for the compute chips. The compute load that the data center can effectively deliver is a function of the complex response of these systems including the associated controls for the load dispatch strategy and cascaded controls of the various subsystems. System modeling with representation of the physical systems and key controls is a critical tool for understanding the physical response and operation of data centers.

This talk presents two different uses cases for system modeling in data centers. The first case focuses on data center operation. High level transient models in Dyad, Julia, and ModelingToolkit of the data center load, power generation, and electrical system are shown focusing primarily on power demand and supply and high-level control and dispatch. These models are meant to capture the critical interactions between the total power demand from the compute side of the data center and the required power generation provided by the turbines and generators. Different operational strategies for turbine scheduling will be demonstrated to assess their impact on system performance and robustness over different load profiles. These models can address questions regarding the optimal dispatch strategy for the turbines and the impact of different load management strategies on system performance. Models including the effects of battery energy storage systems are developed to assess the impact of battery sizing and control strategies on the data center operation. The impacts of various failures can also be simulated with these models. These system models are suitable for simulations over multiple time scales. Shorter simulations are shown to focus on load planning and the resulting transient power dynamics. Long time horizon simulations (hours, weeks, months) support operational and economic optimization of data centers with SciML techniques.

The second use case focuses on multi-physics models for data center cooling. Transient models for data center cooling are demonstrated that capture the thermal interactions between the CPU and GPU and the resulting cooling system. Built from reusable components in Dyad, these models are full physical models that capture the lumped thermal dynamics of the chips and cooling system at the server and rack level. They can provide temperature predictions at the lumped chip level to support a higher level of fidelity in the system simulations and for load planning. These models are still suitable for long time horizon simulations as they are lumped but discretized.

There has been a rapid spread of AI agents into all aspects of society. The exponential increase in capabilities of these agents has led to new ways of working across nearly every profession. While the adoption of LLMs and agentic workflows has been more common in computer science and software development, the integration of these technologies into engineering system simulation tools is at its infancy. As an engineer with over 25 years of experience in model-based systems engineering across different engineering domains, I have had no previous experience with agentic workflows in my daily work prior to the last few months. Speaking with engineering simulation colleagues in different fields, many of them are in the same situation given that their traditional tools did not offer these capabilities but are now ready and interested to explore possibilities of these emerging technologies.

This talk will offer practical perspectives on agentic workflows focused on engineering system simulation use cases. Using the Dyad AI agent, these use cases will be explored using Dyad, Julia, and ModelingToolkit. The focus of this talk is to provide practical perspectives on agentic workflows in model creation, system model assembly, debugging, testing, and simulation and analysis. The examples will also explore different methods for providing resources to the agent to support the tasks required. Examples in different engineering domains will be presented. Various workflows are critically evaluated to assess effectiveness and accuracy. The focus of this talk is to provide practical perspectives demonstrating use cases that work well, those that are still developing or not yet mature (though certainly might be in future versions of the underlying LLMs), and effective prompting techniques based on personal experience. This talk will be presented via slides documenting the workflows along with live demonstrations within the time constraints of the talk.

The capabilities of engineering simulation tools are rapidly changing and are fundamentally redefining the human and machine interface. Engineers need to quickly adapt to utilize new technologies effectively and responsibly. Though it is certainly impossible to gain a deep understanding of agentic workflows within the duration of a single talk, the hope is that this talk demystifies the use of agentic workflows in system simulation within the Julia ecosystem, inspires critical thinking within the context of an engineer’s unique workflows and simulation needs, and provides practical perspectives that can lead to more effective usage as engineers start to adopt these new technologies.