Shape optimization in laminar flow with a label-guided variational autoencoder

Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically. In this work, we explore a Bayesian optimization approach to minimize an object's drag coefficient in laminar flow based on predicting drag directly from the object shape. Jointly training an architecture combining a variational autoencoder mapping shapes to latent representations and Gaussian process regression allows us to generate improved shapes in the two dimensional case we consider.Comment: Contribution to workshop "Bayesian optimization for science and engineering" at NIPS 201

Generative Modeling for Atmospheric Convection

To improve climate modeling, we need a better understanding of multi-scale atmospheric dynamics--the relationship between large scale environment and small-scale storm formation, morphology and propagation--as well as superior stochastic parameterization of convective organization. We analyze raw output from ~6 million instances of explicitly simulated convection spanning all global geographic regimes of convection in the tropics, focusing on the vertical velocities extracted every 15 minutes from ~4 hundred thousands separate instances of a storm-permitting moist turbulence model embedded within a multi-scale global model of the atmosphere. Generative modeling techniques applied on high-resolution climate data for representation learning hold the potential to drive next-generation parameterization and breakthroughs in understanding of convection and storm development. To that end, we design and implement a specialized Variational Autoencoder (VAE) to perform structural replication, dimensionality reduction and clustering on these cloud-resolving vertical velocity outputs. Our VAE reproduces the structure of disparate classes of convection, successfully capturing both their magnitude and variances. This VAE thus provides a novel way to perform unsupervised grouping of convective organization in multi-scale simulations of the atmosphere in a physically sensible manner. The success of our VAE in structural emulation, learning physical meaning in convective transitions and anomalous vertical velocity field detection may help set the stage for developing generative models for stochastic parameterization that might one day replace explicit convection calculations.Comment: 8 Pages, 6 Figures. Submitted to Climate Informatics 202