[HTML][HTML] A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems
In this work, we review the state of the art on approaches for the determination of reaction
networks and the real-time monitoring of reactions in complex chemical systems consisting …
networks and the real-time monitoring of reactions in complex chemical systems consisting …
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering
sophisticated control policies that interact in environments with complicated dynamics, such …
sophisticated control policies that interact in environments with complicated dynamics, such …
When machine learning meets multiscale modeling in chemical reactions
Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of
traditional machine learning algorithms may face many difficulties. In this study, through two …
traditional machine learning algorithms may face many difficulties. In this study, through two …
Error-Controlled Coarse-Graining Dynamics with Mean-Field Randomization
C Liu, J Wang - Journal of Chemical Theory and Computation, 2023 - ACS Publications
In order to comprehend the stochastic behavior of biological systems, it is essential to
accurately infer the dynamics of chemical reaction networks. However, computation of the …
accurately infer the dynamics of chemical reaction networks. However, computation of the …
[PDF][PDF] Digital Chemical Engineering
A Puliyanda, K Srinivasan, K Sivaramakrishnan… - researchgate.net
abstract In this work, we review the state of the art on approaches for the determination of
reaction networks and the real-time monitoring of reactions in complex chemical systems …
reaction networks and the real-time monitoring of reactions in complex chemical systems …