Step Sequence

Espresso ELN is a fully featured electronic lab notebook (ELN) suited for organic, organometallic, polymer and peptide chemistry, featuring a strong protocol editor, detection of synthetic context, substructure searches and optional data sharing and backup via server. It combines simple and intuitive handling with state of the art and beyond interface and functionality and is suited for all needs, ranging from universities and small companies to big pharma teams.

The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.