Simulating complex physical systems often involves solving partial differential equations
(PDEs) with some closures due to the presence of multi-scale physics that cannot be fully …

We present a mathematically well-founded approach for the synthetic modeling of turbulent
flows using generative adversarial networks (GAN). Based on the analysis of chaotic …

Simulation-based inference (SBI) refers to statistical inference on stochastic models for
which we can generate samples, but not compute likelihoods. Like SBI algorithms …

Stochastic parameterizations account for uncertainty in the representation of unresolved
subgrid processes by sampling from the distribution of possible subgrid forcings. Some …

We developed a new class of physics-informed generative adversarial networks (PI-GANs)
to solve forward, inverse, and mixed stochastic problems in a unified manner based on a …

We present a deep learning framework for quantifying and propagating uncertainty in
systems governed by non-linear differential equations using physics-informed neural …

Abstract The instability of Generative Adversarial Network (GAN) training has frequently
been attributed to gradient descent. Consequently, recent methods have aimed to tailor the …

Here we employ and adapt the image-to-image translation concept based on conditional
generative adversarial networks (cGAN) for learning a forward and an inverse solution …

In computational fluid dynamics, there is an inevitable trade off between accuracy and
computational cost. In this work, a novel multi-fidelity deep generative model is introduced …

The goal of this paper is not to introduce a single algorithm or method, but to make
theoretical steps towards fully understanding the training dynamics of generative adversarial …