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Found 5 papers, 5 papers with code
Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks
The approach incorporated the CRNN model into the framework of neural ordinary differential equations to predict the residual mass in TGA data.
Inference of cell dynamics on perturbation data using adjoint sensitivity
While there is no analogous ground truth for real life biological systems, this work demonstrates the ability to construct and parameterize a considerable diversity of network models with high predictive ability.
Stiff Neural Ordinary Differential Equations
We first show the challenges of learning neural ODE in the classical stiff ODE systems of Robertson's problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems.
Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network
Here, we present a neural network approach that autonomously discovers reaction pathways from the time-resolved species concentration data.
Uncertainty Propagation in Deep Neural Network Using Active Subspace
This work employs a gradient-based subspace method and response surface technique to accelerate the uncertainty propagation in DNN.
