Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
This study presents a broad view of the current state of the art of ML applications in the
manufacturing sectors that have a considerable impact on sustainability and the …
manufacturing sectors that have a considerable impact on sustainability and the …
A hybrid science‐guided machine learning approach for modeling chemical processes: A review
This study presents a broad perspective of hybrid process modeling combining the scientific
knowledge and data analytics in bioprocessing and chemical engineering with a science …
knowledge and data analytics in bioprocessing and chemical engineering with a science …
Physics-informed learning of governing equations from scarce data
Harnessing data to discover the underlying governing laws or equations that describe the
behavior of complex physical systems can significantly advance our modeling, simulation …
behavior of complex physical systems can significantly advance our modeling, simulation …
Simulation intelligence: Towards a new generation of scientific methods
A Lavin, D Krakauer, H Zenil, J Gottschlich… - ar** an accurate first-principle model is an important step in employing systems
biology approaches to analyze an intracellular signaling pathway. However, an accurate first …
biology approaches to analyze an intracellular signaling pathway. However, an accurate first …
Introducing hybrid modeling with time-series-transformers: A comparative study of series and parallel approach in batch crystallization
Given the hesitance surrounding the direct implementation of black-box tools due to safety
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …
and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins …
Operable adaptive sparse identification of systems: Application to chemical processes
Over the past few decades, several data‐driven methods have been developed for
identifying a model that accurately describes the process dynamics. Lately, sparse …
identifying a model that accurately describes the process dynamics. Lately, sparse …