1 code implementation • 13 Oct 2023 • Medha Sawhney, Bhas Karmarkar, Eric J. Leaman, Arka Daw, Anuj Karpatne, Bahareh Behkam
Herein, we report Motion Enhanced Multi-level Tracker (MEMTrack), a robust pipeline for detecting and tracking microrobots using synthetic motion features, deep learning-based object detection, and a modified Simple Online and Real-time Tracking (SORT) algorithm with interpolation for tracking.
no code implementations • 21 Aug 2023 • M. Maruf, Arka Daw, Amartya Dutta, Jie Bu, Anuj Karpatne
Furthermore, we propose random cropping as a stochastic aggregation technique that improves the performance of saliency, making it a strong alternative to CAM for WS3.
no code implementations • 2 Nov 2022 • Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente Klein
Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change.
1 code implementation • 5 Jul 2022 • Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne
In this paper, we provide a novel perspective of failure modes of PINNs by hypothesizing that training PINNs relies on successful "propagation" of solution from initial and/or boundary condition points to interior points.
1 code implementation • NeurIPS 2021 • Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne
We also theoretically show that the learning objective of DAM is directly related to minimizing the L0 norm of the masking layer.
1 code implementation • 6 Jun 2021 • Arka Daw, M. Maruf, Anuj Karpatne
In scientific applications, it is also important to inform the learning of DL models with knowledge of physics of the problem to produce physically consistent and generalized solutions.
1 code implementation • 2 Sep 2020 • 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 data in an end-to-end learning fashion.
1 code implementation • 6 Nov 2019 • Arka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, Anuj Karpatne
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture.
2 code implementations • 31 Oct 2017 • Arka Daw, Anuj Karpatne, William Watkins, Jordan Read, Vipin Kumar
This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery.