Rice University

This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.

This work applies scientific multi-agent reinforcement learning (SMARL) to subgrid-scale closure modeling of geophysical turbulence: using the enstrophy spectrum estimated from a few high-fidelity samples as reward, it learns Leith model coefficients online, enabling LES with 160 to 163,840 times coarser resolution than DNS to run stably for simulations about 2000 times the length of the training data and to reproduce DNS kinetic energy spectra and vorticity probability density functions, including the tails that represent extreme events.

This work introduces a modular, large language model (LLM)-powered agent pipeline that automatically extracts and structures composition–process–property attributes of concrete materials from tables and text in scientific publications, achieving F1 scores up to 0.98 across 17 open and proprietary models and, within about one hour, extracting over 10,000 records from 278 papers screened from more than 27,000 publications; after postprocessing this yields the largest open laboratory database for blended cement concrete with nearly 9,000 high-quality records and over 100 attributes, and machine learning analyses indicate that large, diverse, information-rich datasets improve both in-distribution accuracy and out-of-distribution generalization to unseen materials systems.

This work introduces a modular, large language model (LLM)-powered agent pipeline that automatically extracts and structures composition–process–property attributes of concrete materials from tables and text in scientific publications, achieving F1 scores up to 0.98 across 17 open and proprietary models and, within about one hour, extracting over 10,000 records from 278 papers screened from more than 27,000 publications; after postprocessing this yields the largest open laboratory database for blended cement concrete with nearly 9,000 high-quality records and over 100 attributes, and machine learning analyses indicate that large, diverse, information-rich datasets improve both in-distribution accuracy and out-of-distribution generalization to unseen materials systems.