![]() We further illustrate that this approach can be extended towards human-in-the-loop autonomous experiments, where human operators make high-level decisions at high latencies setting the policies for AE, and the ML algorithm performs low-level fast decisions. This approach yields real-time and post-acquisition indicators of the progression of an active learning process interacting with an experimental system. Here, we introduce and realize strategies for post-acquisition forensic analysis applied to the deep kernel learning based AE scanning probe microscopy. ![]() The broad adoption of machine learning (ML)-based automated and autonomous experiments (AE) in physical characterization and synthesis requires development of strategies for understanding and intervention in the experimental workflow.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |