{"total":12,"rows":[{"id":"2501.12215","title":"Automatic selection of the best neural architecture for time series forecasting","created":"2025-01-21","cited_by_count":2,"area":null},{"id":"2507.23428","title":"Merging Memory and Space: A State Space Neural Operator","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2509.26186","title":"PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2510.03989","title":"A Mathematical Explanation of Transformers","created":"2025-10-05","cited_by_count":2,"area":null},{"id":"2605.25413","title":"Autoregression-Free Neural Operators for Time-Dependent PDEs","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2506.11328","title":"An Attention-based Spatio-Temporal Neural Operator for Evolving Physics","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2605.10451","title":"Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2601.17090","title":"SFO: Learning PDE Operators via Spectral Filtering","created":"2026-01-23","cited_by_count":0,"area":null},{"id":"2602.05187","title":"SpectraKAN: Conditioning Spectral Operators","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2605.09016","title":"CATO: Charted Attention for Neural PDE Operators","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.18606","title":"PACE-FNO: Physics-Aligned Canonical Equivariance for Fourier Neural Operators","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2610.00515","title":"Fractional Laplace Neural Operators: Exact Architectures, an Expressivity Frontier at Criticality, and Certified Stability for Memory-Driven Network Dynamics","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}