The way we model systems shapes how we think, simulate, and innovate. Traditional causal modeling defines explicit signal flow from inputs to outputs. While useful, this approach is rigid as systems grow in size and complexity. Acausal modeling, by contrast, focuses on the physical relationships between components and lets the computer derive the equations automatically.
In this webinar, we’ll explore both approaches using simple electrical systems, showing how adding complexity - like extending an RC to an RLC circuit - highlights the scalability and reusability advantages of the acausal paradigm.
You’ll learn how Dyad, built on Julia and SciML, makes acausal modeling practical at scale by bridging physics-based design with modern simulation and analysis.
Join us to see why acausal modeling isn’t just a theoretical improvement - it’s a smarter, faster, and more flexible way to engineer real-world systems.
