3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
GenUQ: Predictive Uncertainty Estimates via Generative Hyper-Networks
Tian Yu Yen, Reese E. Jones, Ravi G. Patel
Operator learning is a recently developed generalization of regression to mappings between functions. It promises to drastically reduce expensive numerical integration of PDEs to f…
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta, Reese E. Jones, Ravi G. Patel +4
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differ…
Mixture of neural operator experts for learning boundary conditions and model selection
Dwyer Deighan, Jonas A. Actor, Ravi G. Patel
While Fourier-based neural operators are best suited to learning mappings between functions on periodic domains, several works have introduced techniques for incorporating non triv…
Analog Bayesian neural networks are insensitive to the shape of the weight distribution
Ravi G. Patel, T. Patrick Xiao, Sapan Agarwal +1
Recent work has demonstrated that Bayesian neural networks (BNN's) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of m…
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
Jeremiah Hauth, Cosmin Safta, Xun Huan +2
The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at mode…