12 citations · 20 across the 5 of their papers we have counts for
6 papers
Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention
Akash Yadav, Taiwo A. Adebiyi, Ruda Zhang
Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs and provide limited support f…
Nonparametric Stochastic Subspaces via the Bootstrap Method for Characterizing Model Error
Akash Yadav, Ruda Zhang
Reliable forward uncertainty quantification in engineering requires methods that account for aleatory and epistemic uncertainties. In many applications, epistemic effects arising f…
Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Models
Akash Yadav, Ruda Zhang
Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimization under uncertainty can be comp…
Stochastic Subspace via Probabilistic Principal Component Analysis for Characterizing Model Error
Akash Yadav, Ruda Zhang
This paper proposes a probabilistic model of subspaces based on the probabilistic principal component analysis (PCA). Given a sample of vectors in the embedding space -- commonly k…
Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization
Ruda Zhang, Negin Alemazkoor
In system analysis and design optimization, multiple computational models are typically available to represent a given physical system. These models can be broadly classified as hi…
Newton retraction as approximate geodesics on submanifolds
Ruda Zhang
Efficient approximation of geodesics is crucial for practical algorithms on manifolds. Here we introduce a class of retractions on submanifolds, induced by a foliation of the ambie…