Abstract: The present disclosure generally relates to handling data of non-linear, multi-variable complex systems. More particularly, the present disclosure relates to methods and systems for training machine learning-based computing devices to ensure adaptive sampling of highly complex data packets. The present invention provides a robust and effective solution to implement a complexity-based sampling methodology that trains the neural network in complex mapping regions, by iteratively sampling the DBMS function and training the neural network in complex regions. The system (110) for training the complex and non-linear neural network may be equipped with a Machine Learning (ML) Engine (214) to solve the problem efficiently.

Abstract: The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for facilitating creation of a digital twin of a process unit which can perform constrained optimization of control parameters to minimize or maximize an objective function. The system can capture non-linearities of the industrial process while the current Industrial Process models try to approximate non-linear process using linear approximation, which are not as accurate as Neural Networks. The proposed system can further create an end-to-end differentiable digital twin model of a process unit, and uses gradient flows for optimization as compared to other digital twin models that are gradient-free.

Abstract: The present disclosure generally relates to handling data of non-linear, multi-variable complex systems. More particularly, the present disclosure relates to methods and systems for training machine learning-based computing devices to ensure adaptive sampling of highly complex data packets. The present invention provides a robust and effective solution to implement a complexity-based sampling methodology that trains the neural network in complex mapping regions, by iteratively sampling the DBMS function and training the neural network in complex regions. The system (110) for training the complex and non-linear neural network may be equipped with a Machine Learning (ML) Engine (214) to solve the problem efficiently.