Data-driven control of nonlinear dynamic systems has emerged as a transformative paradigm in control engineering, leveraging empirical data rather than detailed first-principles models. By embedding ...

A research team has developed a novel method for estimating the predictability of complex dynamical systems. Their work, "Time-lagged recurrence: A data-driven method to estimate the predictability of ...

In the modelic control paradigm, the first step is to establish a dynamic model through system identification. This model offers a continuous but inaccurate description of state transition information ...

Modern industrial systems are becoming increasingly complex due to cloud-native architectures and distributed services. Let's ...

In the race to net-zero emissions, real-time data is the unsung hero. Event-driven systems—powered by technologies like Apache Kafka—are transforming how industries manage energy, optimize resources ...

Learn how systems engineering is shifting from document-centric practices to model-based, data-driven approaches that reduce ...

AI can be added to legacy motion control systems in three phases with minimal disruption: data collection via edge gateways, non-interfering anomaly detection and supervisory control integration.

How event-driven design can overcome the challenges of coordinating multiple AI agents to create scalable and efficient reasoning systems. While large language models are useful for chatbots, Q&A ...