Leveraging FAIR Data to Accelerate the Discovery of Alloys with Optimal Melting Temperatures Using Active Learning and Molecular Dynamics Simulations
FAIR data and workflows can significantly accelerate materials discovery by enabling efficient reuse of prior knowledge and optimization of simulation parameters, as demonstrated by a 10x speedup in identifying alloys with optimal melting temperatures using active learning and molecular dynamics simulations.
中性子星合体における高速フレーバー変換の漸近状態予測
本稿では、中性子星合体における高速ニュートリノ風味不安定性の漸近状態を予測するための、新しい3次元解析モデルを含む複数の解析的混合スキームと、新しい機械学習（ML）モデルを紹介し、その精度を古典的な中性子星合体シミュレーションから抽出された条件からの数千もの局所的なニュートリノ進化の動的計算の結果と比較しています。
Predicting the Asymptotic State of Fast Flavor Transformation in Neutron Star Mergers Using Machine Learning and Analytical Models
This research investigates the effectiveness of machine learning and analytical models in predicting the final state of fast flavor instabilities (FFIs) in dense astrophysical environments like neutron star mergers, aiming to develop efficient subgrid models for large-scale simulations.
基於機器學習的伽瑪射線暴宇宙學模型約束
本研究利用機器學習演算法校準 Pantheon+ 超新星樣本的伽瑪射線暴光度關係，並結合高紅移伽瑪射線暴數據和最新的哈勃觀測數據，對宇宙學模型進行約束，發現機器學習方法在精度上與高斯過程方法具有競爭力。
기계 학습을 이용한 우주론적 모델에 대한 모델 독립적인 감마선 폭발 제약
기계 학습 알고리즘을 사용하여 감마선 폭발의 광도 관계를 보정함으로써 우주론적 모델을 모델 독립적인 방식으로 제약할 수 있습니다.
機械学習を用いた宇宙論モデルに対するモデル非依存ガンマ線バーストの制限
機械学習を用いてガンマ線バーストの光度関係を較正することで、宇宙論モデルを制限できる。
Calibration of the Amati Relation for Gamma-Ray Bursts Using Machine Learning and Its Application to Constraining Cosmological Models
Machine learning algorithms, specifically KNN and RF, can effectively calibrate the Amati relation for gamma-ray bursts using supernovae data, providing competitive results to Gaussian Processes and offering a promising tool for constraining cosmological models.
Estimating the Mass Distribution of Merging Black Hole Binaries from Gravitational Wave Observations Using an Iterative Density Estimation Method
This research introduces a novel iterative density estimation method to accurately reconstruct the mass distribution of merging black hole binaries from gravitational wave observations, revealing a weaker preference for near-equal mass binaries than previously thought and suggesting a more complex relationship between primary and secondary black hole masses.
基於分位數迴歸的模擬推論：一種新的模擬推論方法
本文介紹了一種新的模擬推論方法——神經分位數估計（NQE），它基於條件分位數迴歸，並在各種基準問題上達到了最先進的性能。
분위 회귀를 사용한 시뮬레이션 기반 추론 및 편향 보정 기법 소개
본 논문에서는 조건부 분위 회귀를 기반으로 하는 새로운 시뮬레이션 기반 추론(SBI) 방법인 신경망 분위 추정(NQE)을 제시하며, 제한된 시뮬레이션 예산 및 모델 오류 지정 문제를 해결하기 위해 사후 처리 보정 단계를 통합하여 추론 결과의 정확성을 향상시킵니다.
