3 papers
q-bio.QM2026
PEtab SciML: an exchange format for specifying and training dynamic scientific machine learning models
Sebastian Persson, Branwen Snelling, Maren Philipps +5
Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications…
q-bio.QM2026
Curriculum Multiple Shooting for Robust Training of Neural and Universal Differential Equations
Sebastian Persson, Giacomo Fabrini, Branwen Snelling +1
Neural ordinary differential equations (NODEs) and universal differential equations (UDEs) provide flexible and popular frameworks for learning interpretable dynamical systems from…
q-bio.QM2021
A protocol for dynamic model calibration
Alejandro F. Villaverde, Dilan Pathirana, Fabian Fröhlich +2
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have man…