Veracity (uncertainty of data quality) and variety (heterogeneity of form and meaning of data) are two of the 4V challenges of Big Data. Both are issues for the FAIRness of materials-science results, concerning in particular, the interoperability, i.e., the “I” in FAIR. I will address what may enable us to use heterogenous data for machine learning, e.g. data from different sources or...
Inverse design problems in photonics typically operate in very high dimensional parameter spaces which are notoriously difficult to navigate to find local or global optima. Even worse, from practice it is known that different devices can have comparable performance leading to multimodal device distributions. This often confuses optimization routines causing oscillations and failure to...
