Physics-informed recurrent neural networks and hyper-parameter optimization for dynamic process systems

Modeling Phosphorous Dynamics in a Wastewater Treatment Process using Bayesian Optimized LSTM

(2022) Laura Debel Hansen et al.   COMPUTERS & CHEMICAL ENGINEERING

HybridML: Open Source Platform for Hybrid Modeling

(2022) Kilian Merkelbach et al.   COMPUTERS & CHEMICAL ENGINEERING

Maximizing information from chemical engineering data sets: Applications to machine learning

(2022) Alexander Thebelt et al.   CHEMICAL ENGINEERING SCIENCE

Physics-Based Neural Networks for Simulation and Synthesis of Cyclic Adsorption Processes

(2022) Sai Gokul Subraveti et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

Hybrid differential equations: Integrating mechanistic and data-driven techniques for modelling of water systems

(2022) Ward Quaghebeur et al.   WATER RESEARCH

Recurrent Neural-Network-Based Model Predictive Control of a Plasma Etch Process

(2021) Tianqi Xiao et al.   INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

(2020) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Estimating model inadequacy in ordinary differential equations with physics-informed neural networks

(2020) Felipe A.C. Viana et al.   COMPUTERS & STRUCTURES

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

(2015) Hong Guo et al.   JOURNAL OF ENVIRONMENTAL SCIENCES

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

(2013) Moritz von Stosch et al.   COMPUTERS & CHEMICAL ENGINEERING