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Chaos (Woodbury, N.Y.)|July 18, 2025
Training stiff neural ordinary differential equations with explicit rational Taylor series methodsColby Fronk, Linda PetzoldChaos (Woodbury, N.Y.)|December 13, 2024
Training stiff neural ordinary differential equations with implicit single-step methodsColby Fronk, Linda PetzoldChaos (Woodbury, N.Y.)|November 7, 2025
The vanishing gradient problem for stiff neural differential equationsColby Fronk, Linda PetzoldChaos (Woodbury, N.Y.)|March 25, 2025
Training stiff neural ordinary differential equations with explicit exponential integration methodsColby Fronk, Linda PetzoldJournal of Biological Rhythms|August 8, 2018
Model-based Inference of a Directed Network of Circadian NeuronsDavid McBride, Linda PetzoldIEEE/ACM Transactions on Computational Biology and Bioinformatics|August 30, 2021
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian ComputationMattias AKesson, Prashant Singh, Fredrik Wrede, et al.Plos Computational Biology|December 15, 2022
Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulationAdrien Coulier, Prashant Singh, Marc Sturrock, et al.Multiscale Modeling & Simulation : a SIAM Interdisciplinary Journal|October 20, 2017
MESOSCOPIC MODELING OF STOCHASTIC REACTION-DIFFUSION KINETICS IN THE SUBDIFFUSIVE REGIMEEmilie Blanc, Stefan Engblom, Andreas Hellander, et al.The Journal of Chemical Physics|May 10, 2013
Perspective: Stochastic algorithms for chemical kineticsDaniel T Gillespie, Andreas Hellander, Linda R PetzoldPhysical Review. E|February 13, 2016
Reaction rates for a generalized reaction-diffusion master equationStefan Hellander, Linda PetzoldPageof 10