{"total":23,"rows":[{"id":"2502.09692","title":"AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers","created":"2025-02-13","cited_by_count":53,"area":null},{"id":"2401.06070","title":"Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses","created":"2024-01-11","cited_by_count":18,"area":null},{"id":"2408.07307","title":"Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery","created":"2024-08-14","cited_by_count":18,"area":null},{"id":"2405.09572","title":"Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing","created":"2024-05-13","cited_by_count":14,"area":null},{"id":"2509.16825","title":"KANO: Kolmogorov-Arnold Neural Operator","created":"2025-09-20","cited_by_count":7,"area":null},{"id":"2410.02136","title":"Disentangled Representation Learning for Parametric Partial Differential Equations","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2505.01060","title":"Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2503.00755","title":"Riemann Tensor Neural Networks: Learning Conservative Systems with Physics-Constrained Networks","created":"2025-03-02","cited_by_count":2,"area":null},{"id":"2509.24627","title":"Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2505.24579","title":"Adaptive Correction for Ensuring Conservation Laws in Neural Operators","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2512.14240","title":"Physically consistent model learning for reaction-diffusion systems","created":"2025-12-16","cited_by_count":1,"area":null},{"id":"2605.28851","title":"Towards a Foundation Model for the Martian Atmosphere","created":"2026-05-16","cited_by_count":1,"area":null},{"id":"2404.19536","title":"Physics-Informed Machine Learning On Polar Ice: A Survey","created":"2024-04-30","cited_by_count":0,"area":null},{"id":"2410.20331","title":"Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators","created":"2024-10-27","cited_by_count":0,"area":null},{"id":"2511.09048","title":"Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks","created":"2025-11-12","cited_by_count":0,"area":null},{"id":"2603.20474","title":"From Data to Laws: Neural Discovery of Conservation Laws Without False Positives","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2603.29237","title":"Stochastic Dimension Implicit Functional Projections for Global Integral Conservation in High-Dimensional PINNs","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.15617","title":"A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2608.18148","title":"Flux-form spatiotemporal neural operators for coarse-grained dynamics of multiscale PDEs","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.24049","title":"Physics-Integrated Operator Learning via Gaussian Splatting Representations","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.34627","title":"Evaluating Dynamical Fidelity through Predictive Structure in Physical Representations","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.37113","title":"Neural Constitutive Learning for Generalized Reaction-Diffusion Systems","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.05570","title":"Poisson-GENERIC Neural Operators: Exact Metriplectic Structure in Function Space via Casimir Entropies","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}