Machine Learning in Heterogeneous Porous Materials

no code implementations • 4 Feb 2022 • Martha D'Eli, Hang Deng, Cedric Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda Howard, Geoerge Karniadakid, Vahid Keshavarzzadeh, Robert M. Kirby, Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre Tartakovsky, Daniel M. Tartakovsky, Hamdi Tchelepi, Bozo Vazic, Hari Viswanathan, Hongkyu Yoon, Piotr Zarzycki

The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research.

BIG-bench Machine Learning

Robust Topology Optimization Using Variational Autoencoders

no code implementations • 19 Jul 2021 • Rini Jasmine Gladstone, Mohammad Amin Nabian, Vahid Keshavarzzadeh, Hadi Meidani

Robust topology optimization (RTO) also incorporates the effect of input uncertainties and produces a design with the best average performance of the structure while reducing the response sensitivity to input uncertainties.

A bandit-learning approach to multifidelity approximation

no code implementations • 29 Mar 2021 • Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan

Multifidelity approximation is an important technique in scientific computation and simulation.