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Found 3 papers, 1 papers with code
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from commercial stakeholders.
Numerical simulation, clustering and prediction of multi-component polymer precipitation
To reduce the required computational costs, we apply machine learning techniques for clustering and consequent prediction of the simulated polymer blend images in conjunction with simulations.
Data-driven surrogate modelling and benchmarking for process equipment
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization.
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