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cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…