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 …
T Miyato, M Koyama… - Advances in Neural …, 2022 - proceedings.neurips.cc
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 …
DW Romero, S Lohit - Advances in Neural Information …, 2022 - proceedings.neurips.cc
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 …