35 citations · 76 across the 10 of their papers we have counts for
10 papers
Physics-guided hierarchical neural networks for Maxwell's equations in plasmonic metamaterials
Sean Lynch, Jacob LaMountain, Bo Fan +5
While machine learning (ML) has found multiple applications in photonics, traditional "black box" ML models typically require prohibitively large training data sets. Generation of…
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
M. Maruf, Arka Daw, Amartya Dutta +2
In recent years, several Weakly Supervised Semantic Segmentation (WS3) methods have been proposed that use class activation maps (CAMs) generated by a classifier to produce pseudo-…
Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation
Jie Bu, Kazi Sajeed Mehrab, Anuj Karpatne
Conditional graph generation tasks involve training a model to generate a graph given a set of input conditions. Many previous studies employ autoregressive models to incrementally…
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling
Arka Daw, Jie Bu, Sifan Wang +2
Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), PINNs can sometimes fail to converge to the correct solution…
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…
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…
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…
Beyond Observed Connections : Link Injection
Jie Bu, M. Maruf, Arka Daw
In this paper, we proposed the \textit{link injection}, a novel method that helps any differentiable graph machine learning models to go beyond observed connections from the input…
CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems
Mohannad Elhamod, Jie Bu, Christopher Singh +5
Physics-guided Neural Networks (PGNNs) represent an emerging class of neural networks that are trained using physics-guided (PG) loss functions (capturing violations in network out…
Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids
Nikhil Muralidhar, Jie Bu, Ze Cao +4
Physics-based simulations are often used to model and understand complex physical systems and processes in domains like fluid dynamics. Such simulations, although used frequently,…