Neural ordinary differential equations (neural ODEs) are a popular family of continuous-
depth deep learning models. In this work, we consider a large family of parameterized ODEs …

Data-driven approaches to modeling physical systems fail to generalize to unseen systems
that share the same general dynamics with the learning domain, but correspond to different …

Gradient-based meta-learning methods have primarily been applied to classical machine
learning tasks such as image classification. Recently, partial differential equation (PDE) …

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …

Recent advances in deep learning for physics have focused on discovering shared
representations of target systems by incorporating physics priors or inductive biases into …

This survey examines the broad suite of methods and models for combining machine
learning with physics knowledge for prediction and forecast, with a focus on partial …

Machine learning methods can be a valuable aid in the scientific process, but they need to
face challenging settings where data come from inhomogeneous experimental conditions …

Neural Ordinary Differential Equations (NODEs) often struggle to adapt to new dynamic
behaviors caused by parameter changes in the underlying system, even when these …

Solving parametric partial differential equations (PDEs) presents significant challenges for
data-driven methods due to the sensitivity of spatio-temporal dynamics to variations in PDE …

In science, we are often interested in obtaining a generative model of the underlying system
dynamics from observed time series. While powerful methods for dynamical systems …