FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
arXiv preprint arXiv:2405.09592 (2024)
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UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
SimpleST is a model-agnostic prompt tuning framework that lets pre-trained spatio-temporal GNNs adapt to distribution shifts in traffic data while keeping all original model weights fixed.
citing papers explorer
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FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
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Efficient Prompt Learning for Traffic Forecasting
SimpleST is a model-agnostic prompt tuning framework that lets pre-trained spatio-temporal GNNs adapt to distribution shifts in traffic data while keeping all original model weights fixed.