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…
stat.CO2025
Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology
Henrik Häggström, Sebastian Persson, Marija Cvijovic +1
The analysis of data from multiple experiments, such as observations of several individuals, is commonly approached using mixed-effects models, which account for variation between…