{"total":26,"rows":[{"id":"2310.02299","title":"Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution","created":"2023-10-03","cited_by_count":20,"area":null},{"id":"2410.21853","title":"Learning Infinitesimal Generators of Continuous Symmetries from Data","created":"2024-10-29","cited_by_count":17,"area":null},{"id":"2405.16756","title":"Symmetry-Informed Governing Equation Discovery","created":"2024-05-27","cited_by_count":15,"area":null},{"id":"2503.23616","title":"Interpretable Machine Learning in Physics: A Review","created":"2025-03-30","cited_by_count":14,"area":null},{"id":"2512.11855","title":"Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry","created":"2025-12-05","cited_by_count":7,"area":null},{"id":"2405.19296","title":"Neural Isometries: Taming Transformations for Equivariant ML","created":"2024-05-29","cited_by_count":6,"area":null},{"id":"2510.01855","title":"Explicit Discovery of Nonlinear Symmetries from Dynamic Data","created":"2025-10-02","cited_by_count":6,"area":null},{"id":"2504.10777","title":"AtlasD: Automatic Local Symmetry Discovery","created":"2025-04-15","cited_by_count":5,"area":null},{"id":"2505.18798","title":"Governing Equation Discovery from Data Based on Differential Invariants","created":"2025-05-24","cited_by_count":4,"area":null},{"id":"2606.24418","title":"Data Augmentation: A Fourier Analysis Perspective","created":"2026-06-23","cited_by_count":4,"area":null},{"id":"2608.08091","title":"Adaptive Symmetry Discovery for Dynamical System Identification","created":"2026-08-08","cited_by_count":3,"area":null},{"id":"2609.07031","title":"Efficient Learning and Symmetry Discovery under Exact Invariances","created":"2026-09-07","cited_by_count":3,"area":null},{"id":"2501.09571","title":"MatrixNet: Learning over symmetry groups using learned group representations","created":"2025-01-16","cited_by_count":2,"area":null},{"id":"2410.05232","title":"SymmetryLens: Unsupervised Symmetry Learning via Locality and Density Preservation","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2503.05993","title":"SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations","created":"2025-03-08","cited_by_count":1,"area":null},{"id":"2504.05174","title":"Learning symmetries in datasets","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2505.13289","title":"RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2510.04133","title":"Modeling Time Series Dynamics with Fourier Ordinary Differential Equations","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2510.25731","title":"LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2512.04015","title":"Learning Group Actions In Disentangled Latent Image Representations","created":"2025-12-03","cited_by_count":0,"area":null},{"id":"2603.07246","title":"LEPA: Learning Geometric Equivariance in Satellite Remote Sensing Data with a Predictive Architecture","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2603.07299","title":"Spectral Discovery of Continuous Symmetries via Generalized Fourier Transforms","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2606.17782","title":"Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.23753","title":"On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2608.01582","title":"LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2609.33527","title":"Discovering Symmetries in Neural Network Parameter Spaces","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}