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Dynamic Modeling of Chemical Reaction Systems with Neural Networks and Hybrid Models

Chemical Engineering & Technology, 1999
A common problem in kinetic modeling of complex chemical reactions is that a rigorous description of the reaction system, e.g., based on elementary reactions, is not possible. This is because either the reaction involves too many reactions and intermediates or the reaction mechanism is not known in sufficient detail. Alternative data-driven modeling, e.
Hans-Jörg Zander, Roland Dittmeyer, Josef Wagenhuber   +2 more
openaire   +1 more source

Hawkes process modelling for chemical reaction networks in a random environment

2023
Abstract Cellular processes are open systems, situated in a heterogeneous context, rather than operating in isolation. Chemical reaction networks (CRNs) whose reaction rates are modelled as external stochastic processes account for the heterogeneous environment when describing the embedded process. A marginal description of the embedded
Mark Sinzger-D’Angelo, Heinz Koeppl
openaire   +1 more source

Advances in Chemical Reaction Network Theory for the Identification of Kinetic Models

IFAC Proceedings Volumes, 2012
Abstract In this work, we illustrate the potential of Chemical Reaction Network Theory for model identification of kinetic models, setting up the basis for a novel method of inverse bifurcation analysis of bistable biochemical systems. The method exploits the structural properties of biochemical networks to infer the kinetic parameters from dose ...
Irene Otero-Muras, Pencho Yordanov, Jörg Stelling   +2 more
openaire   +1 more source

P System Models of Bistable, Enzyme Driven Chemical Reaction Networks

2007
In certain classes of chemical reaction networks (CRN), there may be two stable states. The challenge is to find a model of the CRN such that the stability properties can be predicted. In this paper we consider the problem of building a P-system designed to simulate the CRN in an attempt to determine if the CRN is stable or bistable.
Stanley M. Dunn, Peter Stivers
openaire   +1 more source

Multi-fidelity neural network for uncertainty quantification of chemical reaction models

Combustion and Flame, 2023
Keli Lin, Yiru Wang, Bin Yang
exaly  

Kinetic modeling of CL-20 decomposition by a chemical reaction neural network

Journal of Analytical and Applied Pyrolysis, 2023
Mingjie Wen, Qingzhao Chu, Dongping Chen   +2 more
exaly  

Robust Model Invalidation for Chemical Reaction Networks Using Generalized Moments

2023 62nd IEEE Conference on Decision and Control (CDC), 2023
Theodore W. Grunberg, Domitilla Del Vecchio   +1 more
openaire   +1 more source

Benchmarking chemical neural ordinary differential equations to obtain reaction network-constrained kinetic models from spectroscopic data

Engineering Applications of Artificial Intelligence, 2023
Anjana Puliyanda, Karthik Srinivasan, Zukui Li   +2 more
exaly  

Chemical Reaction Neural Network Modelling of Lignocellulosic Biomass Pyrolysis

SSRN Electronic Journal, 2023
Jiangkuan Xing, Ryoichi Kurose, Kun Luo, Jianren Fan   +3 more
openaire   +1 more source

Required Levels of Catalysis for Emergence of Autocatalytic Sets in Models of Chemical Reaction Systems

International Journal of Molecular Sciences, 2011
Wim Hordijk, Mike Steel, Hordijk Wim
exaly