For industries like Life Sciences, it is challenging to collect a large amount of data with high quality that are needed for machine learning and autonomous control applications. Instead, we settle with simulations where initial research and experiments can be conducted. However, it is often difficult
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 producers to the consumers. The question then is, could we further improve it? From AlphaGo
Our research mission is to bring the best intelligent autonomy to manufacturing. Undoubtably, AI driven industrial control is a big part of it. At NeurIPS 2021 (the most prestigious AI conference), we took part in a competition with two tracks
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, continuous manufacturing leads to less production time, lower energy consumption, greater reliability, and better sustainability.
Quartic.ai today announced that it has developed a highly compute efficient digital twin of a penicillin bioreactor that simulates the industrial-grade Penicillium chrysogenum fermentation. The simulation is 40 times faster than traditional models or other available solutions—this allows for large
“Supply chain agility can be defined as an organization’s ability to profitably manufacture and deliver a broad range of high-quality products and services with short lead times and varying volumes, while providing enhanced value to customers.” – The Hacket Group
Something went wrong. Please refresh the page and/or try again.