AME-TS is a structure-guided sparse MoE foundation model for time series that aligns expert routing with series-level temporal descriptors to achieve strong accuracy-efficiency tradeoffs on GIFT-Eval while improving specialization stability.
Fourier neural operator for parametric partial differential equations
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RealBench is a benchmark for data-driven weather forecasting that enforces operational conditions via a 2025 OOD test set, operational analysis, in-situ observations, and event-specific extreme metrics to expose gaps versus reanalysis-based evaluation.
CTF4Nuclear proposes a common task framework for benchmarking ML methods on nuclear engineering datasets using 12 metrics and a new sparse-measurement system monitoring paradigm.
NeurDE learns the equilibrium closure within a kinetic solver to outperform larger neural models on long-term predictions of nonlinear conservation laws including shocks.
The paper introduces a Common Task Framework for scientific ML, benchmarks it on Kuramoto-Sivashinsky and Lorenz systems, and launches a competition on a global sea surface temperature dataset with holdout data.
citing papers explorer
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AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
AME-TS is a structure-guided sparse MoE foundation model for time series that aligns expert routing with series-level temporal descriptors to achieve strong accuracy-efficiency tradeoffs on GIFT-Eval while improving specialization stability.
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RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
RealBench is a benchmark for data-driven weather forecasting that enforces operational conditions via a 2025 OOD test set, operational analysis, in-situ observations, and event-specific extreme metrics to expose gaps versus reanalysis-based evaluation.
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CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
CTF4Nuclear proposes a common task framework for benchmarking ML methods on nuclear engineering datasets using 12 metrics and a new sparse-measurement system monitoring paradigm.
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Neural equilibria for long-term prediction of nonlinear conservation laws
NeurDE learns the equilibrium closure within a kinetic solver to outperform larger neural models on long-term predictions of nonlinear conservation laws including shocks.
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Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
The paper introduces a Common Task Framework for scientific ML, benchmarks it on Kuramoto-Sivashinsky and Lorenz systems, and launches a competition on a global sea surface temperature dataset with holdout data.