Y et, forecasting beyond the weather timescale is challenging because it deals with problems other than initial condition, including boundary interaction, butterfly effect, and our inherent lack of physical understanding.

This occurs even under stationary data distributions, and its underlying mechanism is poorly understood.

To address this need, we contribute Holistic Evaluation of Multimodal Models ( HEMM), visualized in Figure 1. HEMM, as an evaluation framework, goes beyond conventional lists of datasets to emphasize holistic benchmarking at three levels.

In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting.

These challenges are partially due to a lack of structure or inductive bias in the neural networks typically used in learning the policy. One such form of structure that is commonly observed in multi-agent scenarios is symmetry.

In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups.

We also introduce a novel benchmark for evaluating NNPs in molecular property prediction, Hamiltonian prediction, and conformational optimization tasks.

Context-dependent systems have been widely studied; see Mooij et al.