8 papers
Foundation Inference Models for Ordinary Differential Equations
Maximilian Mauel, Johannes R. Hübers, David Berghaus +2
Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challenging. Current approaches such a…
In-Context Learning of Temporal Point Processes with Foundation Inference Models
David Berghaus, Patrick Seifner, Kostadin Cvejoski +2
Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future even…
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Patrick Seifner, Kostadin Cvejoski, David Berghaus +2
Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by…
On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
David Berghaus, Patrick Seifner, Kostadin Cvejoski +1
Many scientific fields, from medicine to seismology, rely on analyzing sequences of events over time to understand complex systems. Traditionally, machine learning models must be b…
Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
Manuel Hinz, Maximilian Mauel, Patrick Seifner +3
High-dimensional recordings of dynamical processes are often characterized by a much smaller set of effective variables, evolving on low-dimensional manifolds. Identifying these la…
Towards Foundation Inference Models that Learn ODEs In-Context
Maximilian Mauel, Manuel Hinz, Patrick Seifner +2
Ordinary differential equations (ODEs) describe dynamical systems evolving deterministically in continuous time. Accurate data-driven modeling of systems as ODEs, a central problem…