Citing Articles For: Discovering invariants via machine learning

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No title available

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No title available

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Articles Citing

Discovering invariants via machine learning

Machine learning of nonlinear waves: data-driven methods for computer-assisted discovery of equations, symmetries, conservation laws, and integrability

Generation of periodic orbits in the restricted three-body problem with a variational autoencoder

Discovering Multiple Conservation Laws from Trajectories by Machine Learning

Constants of motion network reexamined

Machine learning conservation laws of dynamical systems

Closed-form interpretation of neural network classifiers with symbolic gradients

Machine learning for detection of equivariant finite symmetry groups in dynamical systems

Foundations of Ghost Stability

Data-driven discovery of conservation laws from trajectories via neural deflation

Learning Conservation Laws in Unknown Quantum Dynamics

Finding discrete symmetry groups via machine learning

Interpretable conservation laws as sparse invariants

Beyond dynamics: learning to discover conservation principles

No title available

Machine-assisted discovery of integrable symplectic mappings

Machine learning of independent conservation laws through neural deflation

Discovering conservation laws using optimal transport and manifold learning

Automatedly Distilling Canonical Equations From Random State Data

Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems

Deep learning in bifurcations of particle trajectories

Rotationally equivariant super-resolution of velocity fields in two-dimensional flows using convolutional neural networks

No title available

Questioning the question: Exploring how physical degrees of freedom are retrieved with neural networks

Machine learning conservation laws from differential equations

Machine‐Engineered Active Disorder for Digital Photonics

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