activity
20172021
most citedPrediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

558 citations · 639 across the 6 of their papers we have counts for

collaborators

21 papers

math.ST2021

`Basic' Generalization Error Bounds for Least Squares Regression with Well-specified Models

Karthik Duraisamy

This note examines the behavior of generalization capabilities - as defined by out-of-sample mean squared error (MSE) - of Linear Gaussian (with a fixed design matrix) and Linear L…

math.NA2021

A formal proof of the Lax equivalence theorem for finite difference schemes

Mohit Tekriwal, Karthik Duraisamy, Jean-Baptiste Jeannin

The behavior of physical systems is typically modeled using differential equations which are too complex to solve analytically. In practical problems, these equations are discretiz…

physics.flu-dyn2021

Generalizable Physics-constrained Modeling using Learning and Inference assisted by Feature Space Engineering

Vishal Srivastava, Karthik Duraisamy

This work presents a formalism to improve the predictive accuracy of physical models by learning generalizable augmentations from sparse data. Building on recent advances in data-d…

physics.flu-dyn2021

Sub-grid scale characterization and asymptotic behavior of multi-dimensional upwind schemes for the vorticity transport equations

Daniel Foti, Karthik Duraisamy

We study the sub-grid scale characteristics of a vorticity-transport-based approach for large-eddy simulations. In particular, we consider a multi-dimensional upwind scheme for the…

cs.IT2021

Variational Encoders and Autoencoders : Information-theoretic Inference and Closed-form Solutions

Karthik Duraisamy

This work develops problem statements related to encoders and autoencoders with the goal of elucidating variational formulations and establishing clear connections to information-t…

physics.comp-ph2020

Model Reduction for Multi-Scale Transport Problems using Model-form Preserving Least-Squares Projections with Variable Transformation

Cheng Huang, Christopher R. Wentland, Karthik Duraisamy +1

A projection-based formulation is presented for non-linear model reduction of problems with extreme scale disparity. The approach allows for the selection of an arbitrary, but comp…