8 citations · 29 across the 6 of their papers we have counts for
4 papers · 1 filter
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Yibo Yang, Paris Perdikaris
We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variat…
Physics-informed deep generative models
Yibo Yang, Paris Perdikaris
We consider the application of deep generative models in propagating uncertainty through complex physical systems. Specifically, we put forth an implicit variational inference form…
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yibo Yang, Paris Perdikaris
We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Sp…
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
Yibo Yang, Nicholas Ruozzi, Vibhav Gogate
We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods. The key idea is to…