{"total":33,"rows":[{"id":"2603.13227","title":"Representation Learning for Spatiotemporal Physical Systems","created":"2026-03-13","cited_by_count":11,"area":null},{"id":"2602.00884","title":"Test-time Generalization for Physics through Neural Operator Splitting","created":"2026-01-31","cited_by_count":10,"area":null},{"id":"2602.02788","title":"Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs","created":"2026-02-02","cited_by_count":8,"area":null},{"id":"2606.01470","title":"Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence","created":"2026-05-31","cited_by_count":4,"area":null},{"id":"2603.15431","title":"Physics-informed fine-tuning of foundation models for partial differential equations","created":"2026-03-16","cited_by_count":3,"area":null},{"id":"2605.14546","title":"Discovering Physical Directions in Weight Space: Composing Neural PDE Experts","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2605.15284","title":"Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2602.15004","title":"PDE foundation models are skillful AI weather emulators for the Martian atmosphere","created":"2026-02-16","cited_by_count":2,"area":null},{"id":"2602.18679","title":"Transformers for dynamical systems learn transfer operators in-context","created":"2026-02-21","cited_by_count":2,"area":null},{"id":"2512.23884","title":"Autoregressive long-horizon prediction of plasma edge dynamics","created":"2025-12-29","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":"2606.15015","title":"NEXUS: Neural Energy Fields for Physically Consistent Contact-Rich 3D Object Dynamics","created":"2026-06-12","cited_by_count":1,"area":null},{"id":"2609.36216","title":"Preconditioned Physics-Informed Neural Operator Training","created":"2026-09-28","cited_by_count":1,"area":null},{"id":"2610.01974","title":"Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems","created":"2026-10-01","cited_by_count":1,"area":null},{"id":"2601.12614","title":"Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulations","created":"2026-01-18","cited_by_count":0,"area":null},{"id":"2605.04832","title":"Replay-Based Continual Learning for Physics-Informed Neural Operators","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.05540","title":"Autoregressive One-Step Generative Modeling for Dynamical System Forecasting","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.15399","title":"Breakeven complexity: A new perspective on neural partial differential equation solvers","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.15793","title":"AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.25949","title":"Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.15512","title":"Towards Data-Efficient Cross-Device Generalization of Grad-Shafranov Equilibria via Transfer Learning Neural Operator","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2608.11937","title":"Distillation of Foundation Models for Time-dependent PDEs","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.23626","title":"A survey detection channel overrides the pixels in an astronomical foundation model, and biases tomographic mean redshifts","created":"2026-08-23","cited_by_count":0,"area":null},{"id":"2608.24547","title":"From Numerical Simulators of PDEs to Neural Emulators and Back","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.30198","title":"Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.34715","title":"PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.03363","title":"Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.10398","title":"Cross-Domain Pretraining for Steady-State Neural CFD Surrogates","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.10972","title":"Transferability of Learned States in Neural PDE Solvers","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.12325","title":"Prior or Feedback? What an LLM Uses When Adapting Neural Operators","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.12618","title":"Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data","created":"2026-10-08","cited_by_count":0,"area":null},{"id":"2610.14687","title":"One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.16236","title":"From Simulated Fluids to Living Tissue: Zero-Shot Foundation Model Transfer to Perturbed Embryos","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}