arXiv:2609.08620v1 Announce Type: cross
Abstract: We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learni...
By Yousra El-Bachir, Frederik Van der Veken, Davide di Croce, Carlo Emilio Montanari, Massimo Giovannozzi, Ekaterina Krymova, Tatiana Pieloni
The paper introduces a model calibration method using optimal transport to address discrepancies between simulation and experimental data in high-dimensional machine learning applications. Applied to jet tagging in particle physics, the technique calibrates a 128‑dimensional latent representation from a general‑purpose classifier, ensuring downstream derived quantities are properly calibrated. This enables more reliable use of foundation models for jet flavor analysis in LHC experiments and offers a general framework for correcting high‑dimensional simulations across scientific fields.
By Malte Algren, Tobias Golling, Francesco Armando Di Bello, Christopher Pollard
arXiv:2609.00488v1 Announce Type: new
Abstract: Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamic...
By Prajwal Ananth, Shuwen Yue
arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.
By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak
arXiv:2608.22746v1 Announce Type: new
Abstract: This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distribut...
By Fenglin Zhang, Teyan Liu, Jie Wang
arXiv:2609.36044v1 Announce Type: cross
Abstract: We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the...
By Sean Benevedes, Mani Dehghan, Aishik Ghosh, Tae Hyoun Park