Tag: machine learning

Machine learning and first principle models are two widely discussed approaches for process optimization nowadays. First principle models, which are also referred as dynamic models,......

Our paper titled SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments has recently been accepted by NeurIPS 2022, Datasets and Benchmarks Track. In this......

Machine learning and first principle models are two widely discussed approaches for process optimization nowadays. First principle models, which are also referred as dynamic models, have long been one...

In the last 2 – 3 years, Life Science industry supply chain disruptions highlighted the need for high-performance, distributed manufacturing that can accelerate the delivery......

Modern process control algorithms are the key to the success of industrial automation. The increased efficiency and quality create value that benefits everyone from the......

Our research mission is to bring the best intelligent autonomy to manufacturing. Undoubtedly, AI-driven industrial control is a big part of it. At NeurIPS 2021......

This is a joint work of Benjamin Decardi-Nelson, Jerry Cheng, and Mohan Zhang. The future of manufacturing is continuous and autonomous. Compared to batch manufacturing,......

We showed that when you are early in your digitalization journey where you only have access to manipulated variables (e.g. sugar feed rate) and the outcome (e.g. yield), you......

For many batch processes (e.g. in Life Sciences, Food & Beverage), the Design of Experiments (DoE) is usually conducted before scaling up to production...

This isn’t a comparison between Apache Spark vs. Dask. But if you are interested in that, Dask is humble enough to include that in their......