Graph-based next-step prediction models have recently been very successful in modeling
complex high-dimensional physical systems on irregular meshes. However, due to their …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are sample efficient, and interpretable but often rely on …

Symmetries have been leveraged to improve the generalization of neural networks through
different mechanisms from data augmentation to equivariant architectures. However, despite …

We present a novel method for guaranteeing linear momentum in learned physics
simulations. Unlike existing methods, we enforce conservation of momentum with a hard …

Rotation equivariance is a desirable property in many practical applications such as motion
forecasting and 3D perception, where it can offer benefits like sample efficiency, better …

Physics-informed deep learning has been developed as a novel paradigm for learning
physical dynamics recently. While general physics-informed deep learning methods have …

Although equivariant machine learning has proven effective at many tasks, success
depends heavily on the assumption that the ground truth function is symmetric over the …

Modern applications increasingly require learning and forecasting latent dynamics from high-
dimensional time-series. Compared to univariate time-series forecasting, this adds a new …

Time-evolution of partial differential equations is fundamental for modeling several complex
dynamical processes and events forecasting, but the operators associated with such …

Modelling complex systems, like the human heart, has made great progress over the last
decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias …