32 citations · 41 across the 8 of their papers we have counts for
8 papers
Slimmable NAM: Neural Amp Models with adjustable runtime computational cost
Steven Atkinson
This work demonstrates "slimmable Neural Amp Models", whose size and computational cost can be changed without additional training and with negligible computational overhead, enabl…
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…
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
Steven Atkinson, Yiming Zhang, Liping Wang
There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstra…
Data-based Discovery of Governing Equations
Waad Subber, Piyush Pandita, Sayan Ghosh +3
Most common mechanistic models are traditionally presented in mathematical forms to explain a given physical phenomenon. Machine learning algorithms, on the other hand, provide a m…
Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data
Steven Atkinson
What do data tell us about physics-and what don't they tell us? There has been a surge of interest in using machine learning models to discover governing physical laws such as diff…
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Sayan Ghosh, Piyush Pandita, Steven Atkinson +5
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical resp…
Bayesian task embedding for few-shot Bayesian optimization
Steven Atkinson, Sayan Ghosh, Natarajan Chennimalai-Kumar +2
We describe a method for Bayesian optimization by which one may incorporate data from multiple systems whose quantitative interrelationships are unknown a priori. All general (nonr…
Data-driven discovery of free-form governing differential equations
Steven Atkinson, Waad Subber, Liping Wang +3
We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a…