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 …
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 …
PolyODENet: Deriving mass-action rate equations from incomplete transient kinetics data
Kinetics of a reaction network that follows mass-action rate laws can be described with a
system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it …
system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it …
[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 …
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 …
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …
providing an expressive view of the chemical space and multiscale processes. Their …
Autonomous discovery of unknown reaction pathways from data by chemical reaction neural network
Chemical reactions occur in energy, environmental, biological, and many other natural
systems, and the inference of the reaction networks is essential to understand and design …
systems, and the inference of the reaction networks is essential to understand and design …
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 …
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 …
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 …