4 papers
Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning
Oriol Vendrell-Gallart, Nima Negarandeh, Ramin Bostanabad
Neural operators provide fast surrogates for PDEs but their deterministic predictions limit their use in tasks requiring uncertainty quantification (UQ), especially under geometric…
On the Uncertainty Quantification Ability of Tabular Foundation Models
Tyler R. Johnson, Kian Ben-Jacob, Nima Negarandeh +2
Foundation models (FMs) have achieved substantial success in generalizing across tasks without problemspecific training or fine-tuning. However, many critical applications in mecha…
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
Nima Negarandeh, Carlos Mora, Ramin Bostanabad
Gaussian processes (GPs) are powerful probabilistic models that define flexible priors over functions, offering strong interpretability and uncertainty quantification. However, GP…
A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion
Oriol Vendrell-Gallart, Nima Negarandeh, Zahra Zanjani Foumani +3
Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the poten…