A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes

2 December 2021

Papers citing "A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes"

4 / 4 papers shown

Title
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows Mathis Bode M. Gauding Zeyu Lian D. Denker M. Davidovic K. Kleinheinz J. Jitsev H. Pitsch AI4CE 43 21 0 26 Nov 2019
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling Arka Daw Anuj Karpatne William Watkins J. Read Vipin Kumar PINN 38 533 0 31 Oct 2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions Kristof T. Schütt Pieter-Jan Kindermans Huziel Enoc Sauceda Felix Stefan Chmiela A. Tkatchenko K. Müller 137 1,074 0 26 Jun 2017
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data Anuj Karpatne G. Atluri James H. Faghmous M. Steinbach A. Banerjee A. Ganguly Shashi Shekhar N. Samatova Vipin Kumar AI4CE 51 981 0 27 Dec 2016