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math.NA2025
RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
Hardeep Bassi, Yuanran Zhu, Erika Ye +5
We present RELift (Restrict, Evolve, Lift), a two-phase learning framework that couples coarse-grid numerical solvers with neural operators to super-resolve and forecast fine-grid…
cs.LG2025★ 1 cited
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
Yuan Mi, Pu Ren, Hongteng Xu +6
Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep lear…