Nathan M. Urban | DeepAI

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research

∙ 09/12/2023

Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models

Pharmacodynamic (PD) models are mathematical models of cellular reaction...

0 Natalie M. Isenberg, et al. ∙

research

∙ 09/06/2023

Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks

Bayesian inference for neural networks, or Bayesian deep learning, has t...

0 Sanket Jantre, et al. ∙

research

∙ 06/14/2023

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators

Modern climate projections lack adequate spatial and temporal resolution...

8 Sungduk Yu, et al. ∙

research

∙ 04/27/2020

A Bayesian approach to regional decadal predictability: Sparse parameter estimation in high-dimensional linear inverse models of high-latitude sea surface temperature variabili

Stochastic reduced models are an important tool in climate systems whose...

1 Dallas Foster, et al. ∙