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Found 4 papers, 2 papers with code
RoPINN: Region Optimized Physics-Informed Neural Networks
To mitigate this inherent deficiency of the default scatter-point optimization, this paper proposes and theoretically studies a new training paradigm as region optimization.
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Going beyond the mainstream paradigms of plain decomposition and multiperiodicity analysis, we analyze temporal variations in a novel view of multiscale-mixing, which is based on an intuitive but important observation that time series present distinct patterns in different sampling scales.
Transolver: A Fast Transformer Solver for PDEs on General Geometries
Transformers have empowered many milestones across various fields and have recently been applied to solve partial differential equations (PDEs).
Solving High-Dimensional PDEs with Latent Spectral Models
A burgeoning paradigm is learning neural operators to approximate the input-output mappings of PDEs.
