FD-Bench supplies the first modular, reproducible benchmark and leaderboard for comparing neural PDE solvers on fluid dynamics tasks with direct numerical solver baselines.
A survey of generative techniques for spatial-temporal data mining
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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.
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.
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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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.
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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
A Mamba-plus-attention hybrid with FFT-Laplace and TCN encoding claims state-of-the-art accuracy and efficiency on eight multivariate time-series forecasting benchmarks.