1.1k citations · 1.3k across the 4 of their papers we have counts for
15 papers
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng +6
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durab…
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media
Govinda Anantha Padmanabha, Nicholas Zabaras
Fine-scale simulation of complex systems governed by multiscale partial differential equations (PDEs) is computationally expensive and various multiscale methods have been develope…
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
Navid Shervani-Tabar, Nicholas Zabaras
Recent advances in artificial intelligence have propelled the development of innovative computational materials modeling and design techniques. Generative deep learning models have…
Solving inverse problems using conditional invertible neural networks
Govinda Anantha Padmanabha, Nicholas Zabaras
Inverse modeling for computing a high-dimensional spatially-varying property field from indirect sparse and noisy observations is a challenging problem. This is due to the complex…
Multi-fidelity Generative Deep Learning Turbulent Flows
Nicholas Geneva, Nicholas Zabaras
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…
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in th…