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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “MOOSE”

Reduced Order Modeling of a MOOSE-based Advanced Manufacturing Model with Operator Learningstat.ML

This work develops a fast-running ROM for MOOSE-based AM model using OL.

problem Achieving desired material properties in real-time manufacturing processes.

method Operator learning (OL) and Fourier neural operator for ROM development.

result OL-based ROM outperforms conventional deep neural network-based ROM in benchmark tests.

reduced order modeling operator learning MOOSE advanced manufacturing deep reinforcement learning

2023-08-18abs ↗pdf ↗

Overcoming Model Bias for Robust Offline Deep Reinforcement Learningcs.LG

MOOSE improves offline RL robustness by using dynamics models.

problem Low robustness of model-free offline RL algorithms in industrial settings.

method MOOSE uses dynamics models to assess policy performance, keeping policies within data support.

result MOOSE outperforms state-of-the-art model-free offline RL algorithms in robust performance.

Offline RL Model-free Robustness Dynamics models Policy performance

2020-08-12abs ↗pdf ↗

Fast and Accurate Reduced-Order Modeling of a MOOSE-based Additive Manufacturing Model with Operator Learningcs.LG

This work develops fast and accurate ROMs for AM models using OL methods.

problem Achieving specific material properties in AM by manipulating process parameters increases computational load.

method Operator learning (OL) approach with Fourier neural operator (FNO) and DeepONet.

result OL methods offer comparable performance and outperform DNN in accuracy and generalizability.

reduced-order modeling operator learning additive manufacturing Fourier neural operator DeepONet

2023-08-04abs ↗pdf ↗

MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Searchcs.CL

New method uses LLMs to generate detailed scientific hypotheses.

problem Generating detailed, actionable scientific hypotheses from coarse initial directions.

method Hierarchical search method that incrementally adds details to hypotheses.

result Hierarchical search method consistently outperforms strong baselines on expert-annotated hypotheses.

LLMs scientific hypothesis generation hierarchical search reward landscape fine-grained hypotheses

2025-05-25abs ↗pdf ↗