Modern control system design is increasingly embracing data-driven methodologies, which bypass the traditional necessity for precise process models by utilising experimental input–output data. This ...

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 ...

Streamline Control and Snowflake deliver a unified data foundation that helps energy organizations modernize faster and ...

How Governance-by-Design Frameworks Are Reshaping Responsible AI in Enterprise Systems As artificial intelligence cont ...

How finance and operations leaders can take back control of their telecom spending by using a data-driven approach ...

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 ...

Machine Design’s Motion Systems Takeover Week (Oct. 20–24, 2025) explored how the fusion of mechanical motion and data-driven control is reshaping high-precision applications across industries, from ...

The car industry is evolving with the integration of agentic artificial intelligence (AI) in intelligent vehicles, revolutionizing the car manufacturing process through data-driven design and ...

Many organizations realize the importance of data-driven decision-making, yet few manage to harness the full potential of their data. The list of challenges ranges from fragmented data silos to ...