[2507.10303] MF-GLaM: A multifidelity stochastic emulator using generalized lambda models

Statistics > Machine Learning arXiv:2507.10303 (stat) [Submitted on 14 Jul 2025 (v1), last revised 8 Apr 2026 (this version, v2)] Title:MF-GLaM: A multifidelity stochastic emulator using generalized lambda models Authors:K. Giannoukou, X. Zhu, S. Marelli, B. Sudret View a PDF of the paper titled MF-GLaM: A multifidelity stochastic emulator using generalized lambda models, by K. Giannoukou and 3 other authors View PDF HTML (experimental) Abstract:Stochastic simulators exhibit intrinsic stochasticity due to unobservable, uncontrollable, or unmodeled input variables, resulting in random outputs even at fixed input conditions. Such simulators are common across various scientific disciplines; however, emulating their entire conditional probability distribution is challenging, as it is a task traditional deterministic surrogate modeling techniques are not designed for. Additionally, accurately characterizing the response distribution can require prohibitively large datasets, especially for computationally expensive high-fidelity (HF) simulators. When lower-fidelity (LF) stochastic simulators are available, they can enhance limited HF information within a multifidelity surrogate modeling (MFSM) framework. While MFSM techniques are well-established for deterministic settings, constructing multifidelity emulators to predict the full conditional response distribution of stochastic simulators remains a challenge. In this paper, we propose multifidelity generalized lambda models (MF-G...