The proposed methodology can easily deal with large meshes without the need for explicit shape parameterization and provides crucial predictive uncertainties, which are essential for informed decision-making.

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data.

An artificial intelligence model trained on NASA satellite imagery can forecast what the sun will look like hours into the future – even predicting the appearance of solar flares that may warn of dangerous space weather for Earth. "I love to think of this model as an AI telescope where you can look at the sun and you can understand the moods," says Juan Bernabé-Moreno at IBM Research Europe. The sun's moods matter because outbursts of solar activity can bombard Earth with high-energy particles, X-rays and extreme ultraviolet radiation. These can disrupt GPS and communications satellites, and potentially harm astronauts and even people on commercial airlines. Solar flares can be followed by coronal mass ejections, which may disrupt Earth's own magnetic field and create geomagnetic storms capable of knocking out power grids.

Reinforcement learning algorithms typically consider discrete-time dynamics, even though the underlying systems are often continuous in time.

Breaking down a problem into intermediate steps has demonstrated impressive performance in Large Language Model (LLM) reasoning.

Building on the Benamou-Brenier formula from optimal transport and action matching, we use a variational problem to infer parameter-and time-dependent gradient fields that represent approximations of the population dynamics.

Hands are the primary means through which humans interact with the world.