Recent work on neural algorithmic reasoning has investigated the reasoning capabilities of
neural networks, effectively demonstrating they can learn to execute classical algorithms on …

The use of machine learning to build subgrid parametrizations for climate models is
receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised …

Despite the success of equivariant neural networks in scientific applications, they require
knowing the symmetry group a priori. However, it may be difficult to know which symmetry to …

In this study, we present\textit {meta-sequential prediction}(MSP), an unsupervised
framework to learn the symmetry from the time sequence of length at least three. Our method …

We present a learning algorithm for discovering conservation laws given as sums of
geometrically local observables in quantum dynamics. This includes conserved quantities …

Equivariances provide useful inductive biases in neural network modeling, with the
translation equivariance of convolutional neural networks being a canonical example …

Abstract Group Convolutional Neural Networks (G-CNNs) constrain learned features to
respect the symmetries in the selected group, and lead to better generalization when these …

Drawing on ergodic theory, we introduce a novel training method for machine learning
based forecasting methods for chaotic dynamical systems. The training enforces dynamical …

Many real-world dynamical systems are associated with first integrals (aka invariant
quantities), which are quantities that remain unchanged over time. The discovery and …

We introduce a methodology for seeking conservation laws within a Hamiltonian dynamical
system, which we term “neural deflation.” Inspired by deflation methods for steady states of …