Physics-Enhanced neural ordinary differential equations: Application to industrial chemical reaction systems
Ordinary differential equations (ODEs) are extremely important in modeling dynamic
systems, such as chemical reaction networks. However, many challenges exist for building …
systems, such as chemical reaction networks. However, many challenges exist for building …
[HTML][HTML] Global reaction neural networks with embedded stoichiometry and thermodynamics for learning kinetics from reactor data
T Kircher, FA Döppel, M Votsmeier - Chemical Engineering Journal, 2024 - Elsevier
The digitalization of chemical research and industry is vastly increasing the available data
for developing and parametrizing kinetic models. To exploit this data, machine learning …
for developing and parametrizing kinetic models. To exploit this data, machine learning …
Benchmarking chemical neural ordinary differential equations to obtain reaction network-constrained kinetic models from spectroscopic data
Kinetic model identification relies on accurate concentration measurements and physical
constraints to limit solution multiplicity. Not having these measurements and prior knowledge …
constraints to limit solution multiplicity. Not having these measurements and prior knowledge …
ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers
Solving for detailed chemical kinetics remains one of the major bottlenecks for
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
Generalized reactor neural ODE for dynamic reaction process modeling with physical interpretability
Modeling is essential for designing, scaling up, controlling, and optimizing a reactor or
process involving reactions. However, developing high-fidelity mechanistic models from first …
process involving reactions. However, developing high-fidelity mechanistic models from first …
Kinetics-informed neural networks
Chemical kinetics and reaction engineering consists of the phenomenological framework for
the disentanglement of reaction mechanisms, optimization of reaction performance and the …
the disentanglement of reaction mechanisms, optimization of reaction performance and the …
Kinetics-constrained neural ordinary differential equations: Artificial neural network models tailored for small data to boost kinetic model development
A Fedorov, A Perechodjuk, D Linke - Chemical Engineering Journal, 2023 - Elsevier
Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in
fundamental and applied science. However, training and building reliable ANN models …
fundamental and applied science. However, training and building reliable ANN models …
Deep learning for scalable chemical kinetics
Chemistry is critical to many computational fluid dynamics (CFD) problems, such as
propulsion system design, engine diagnostics, and atmospheric modeling. However, many …
propulsion system design, engine diagnostics, and atmospheric modeling. However, many …
Kinetics parameter optimization of hydrocarbon fuels via neural ordinary differential equations
Chemical kinetics mechanisms are essential for understanding, analyzing, and simulating
complex combustion phenomena. In this study, a neural ordinary differential equation …
complex combustion phenomena. In this study, a neural ordinary differential equation …
Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification
Chemical reaction neural network (CRNN), a recently developed tool for autonomous
discovery of reaction models, has been successfully demonstrated on a variety of chemical …
discovery of reaction models, has been successfully demonstrated on a variety of chemical …