9 citations · 27 across the 12 of their papers we have counts for
12 papers
The Recipe Matters More Than the Kitchen:Mathematical Foundations of the AI Weather Prediction Pipeline
Piyush Garg, Diana R. Gergel, Andrew E. Shao +1
AI weather prediction has advanced rapidly, yet no unified mathematical framework explains what determines forecast skill. Existing theory addresses specific architectural choices…
Elastic turbulence hides in the small scales of inertial polymeric turbulence
Piyush Garg, Marco Edoardo Rosti
Gaining a fundamental understanding of turbulent flows of dilute polymer solutions has been a challenging and outstanding problem for a long time. In this letter, we examine homoge…
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling
Jaideep Pathak, Yair Cohen, Piyush Garg +8
Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme w…
Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
Peter Manshausen, Yair Cohen, Peter Harrington +7
Data assimilation of observational data into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation…
ADSumm: Annotated Ground-truth Summary Datasets for Disaster Tweet Summarization
Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat
Online social media platforms, such as Twitter, provide valuable information during disaster events. Existing tweet disaster summarization approaches provide a summary of these eve…
ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data
Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat
The abundance of situational information on Twitter poses a challenge for users to manually discern vital and relevant information during disasters. A concise and human-interpretab…
DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations
Jason Stock, Jaideep Pathak, Yair Cohen +5
This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and…
TONE: A 3-Tiered ONtology for Emotion analysis
Srishti Gupta, Piyush Kumar Garg, Sourav Kumar Dandapat
Emotions have played an important part in many sectors, including psychology, medicine, mental health, computer science, and so on, and categorizing them has proven extremely usefu…
IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization
Piyush Kumar Garg, Roshni Chakraborty, Srishti Gupta +1
Online social media platforms, such as Twitter, are one of the most valuable sources of information during disaster events. Therefore, humanitarian organizations, government agenci…
PORTRAIT: a hybrid aPproach tO cReate extractive ground-TRuth summAry for dIsaster evenT
Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat
Disaster summarization approaches provide an overview of the important information posted during disaster events on social media platforms, such as, Twitter. However, the type of i…
EnDSUM: Entropy and Diversity based Disaster Tweet Summarization
Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat
The huge amount of information shared in Twitter during disaster events are utilized by government agencies and humanitarian organizations to ensure quick crisis response and provi…
OntoDSumm : Ontology based Tweet Summarization for Disaster Events
Piyush Kumar Garg, Roshni Chakraborty, Sourav Kumar Dandapat
The huge popularity of social media platforms like Twitter attracts a large fraction of users to share real-time information and short situational messages during disasters. A summ…