Kinetics-Informed Neural Networks
- URL: http://arxiv.org/abs/2011.14473v1
- Date: Mon, 30 Nov 2020 00:07:09 GMT
- Title: Kinetics-Informed Neural Networks
- Authors: Gabriel S. Gusm\~ao, Adhika P. Retnanto, Shashwati C. da Cunha, Andrew
J. Medford
- Abstract summary: We use feed-forward artificial neural networks as basis functions for the construction of surrogate models to solve ordinary differential equations.
We show that the simultaneous training of neural nets and kinetic model parameters in a regularized multiobjective optimization setting leads to the solution of the inverse problem.
This surrogate approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chemical kinetics consists of the phenomenological framework for the
disentanglement of reaction mechanisms, optimization of reaction performance
and the rational design of chemical processes. Here, we utilize feed-forward
artificial neural networks as basis functions for the construction of surrogate
models to solve ordinary differential equations (ODEs) that describe
microkinetic models (MKMs). We present an algebraic framework for the
mathematical description and classification of reaction networks, types of
elementary reaction, and chemical species. Under this framework, we demonstrate
that the simultaneous training of neural nets and kinetic model parameters in a
regularized multiobjective optimization setting leads to the solution of the
inverse problem through the estimation of kinetic parameters from synthetic
experimental data. We probe the limits at which kinetic parameters can be
retrieved as a function of knowledge about the chemical system states over
time, and assess the robustness of the methodology with respect to statistical
noise. This surrogate approach to inverse kinetic ODEs can assist in the
elucidation of reaction mechanisms based on transient data.
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