Nathan M. Urban

activity

20222026

most citedMulti-Objective Latent Space Optimization of Generative Molecular Design Models

31 citations · 57 across the 18 of their papers we have counts for

collaborators

Showing 2026Show all

3 papers · 1 filter

physics.acc-ph2026

Machine learning assisted Bayesian calibration of an accelerator digital twin from orbit response data

Christopher Kelly, Kevin A. Brown, Georg H. Hoffstaetter +2

Digital twins of particle accelerators are used to plan and control operations and to design data collection campaigns. Accurate modeling typically requires knowledge of quantities…

math.DS2026

Flexible and Stable Dynamics Discovery with Onsager's Variational Principle

Anthony Gruber, Ritoban Roy-Chowdhury, Irina Tezaur +1

Variational principles specify the dynamics of a physical system via the extremization of associated functional data. Onsager's variational principle (OVP), which characterizes dis…

cond-mat.str-el2026

Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the $t$ - $t^{'}$ - $t^{''}$ - $J$ model

Sanket Jantre, Nathan M. Urban, Weiguo Yin +1

Spectral functions encode key many-body information but are costly to compute with high fidelity. Machine-learning surrogates have emerged as a powerful alternative, yet many appro…

Machine learning assisted Bayesian calibration of an accelerator digital twin from orbit response data

Flexible and Stable Dynamics Discovery with Onsager's Variational Principle

Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the t-t′-t′′-J model

Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the $t$ - $t^{'}$ - $t^{''}$ - $J$ model