1.7k citations · 2.5k across the 48 of their papers we have counts for
6 papers · 1 filter
Physics-Informed Machine Learning for Optical Modes in Composites
Abantika Ghosh, Mohannad Elhamod, Jie Bu +3
We demonstrate that embedding physics-driven constraints into machine learning process can dramatically improve accuracy and generalizability of the resulting model. Physics-inform…
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation
Austin Chennault, Andrey A. Popov, Amit N. Subrahmanya +4
Data assimilation is the process of fusing information from imperfect computer simulations with noisy, sparse measurements of reality to obtain improved estimates of the state or p…
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)
Jie Bu, Arka Daw, M. Maruf +1
A central goal in deep learning is to learn compact representations of features at every layer of a neural network, which is useful for both unsupervised representation learning an…
PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics
Arka Daw, M. Maruf, Anuj Karpatne
As applications of deep learning (DL) continue to seep into critical scientific use-cases, the importance of performing uncertainty quantification (UQ) with DL has become more pres…
A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning
Reza Sepasdar, Anuj Karpatne, Maryam Shakiba
An image-based deep learning framework is developed in this paper to predict damage and failure in microstructure-dependent composite materials. The work is motivated by the comple…
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs
Jie Bu, Anuj Karpatne
We propose quadratic residual networks (QRes) as a new type of parameter-efficient neural network architecture, by adding a quadratic residual term to the weighted sum of inputs be…