24 citations · 41 across the 15 of their papers we have counts for
15 papers
Learning to forecast diagnostic parameters using pre-trained weather embedding
Peetak P. Mitra, Vivek Ramavajjala
Data-driven weather prediction (DDWP) models are increasingly becoming popular for weather forecasting. However, while operational weather forecasts predict a wide variety of weath…
HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns
Subhashis Hazarika, Haruki Hirasawa, Sookyung Kim +6
Clouds have a significant impact on the Earth's climate system. They play a vital role in modulating Earth's radiation budget and driving regional changes in temperature and precip…
Verification against in-situ observations for Data-Driven Weather Prediction
Vivek Ramavajjala, Peetak P. Mitra
Data-driven weather prediction models (DDWPs) have made rapid strides in recent years, demonstrating an ability to approximate Numerical Weather Prediction (NWP) models to a high d…
Improving CFD simulations by local machine-learned correction
Peetak Mitra, Majid Haghshenas, Niccolo Dal Santo +2
High-fidelity computational fluid dynamics (CFD) simulations for design space explorations can be exceedingly expensive due to the cost associated with resolving the finer scales.…
Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Soo Kyung Kim, Kalai Ramea, Salva Rühling Cachay +6
The availability of training data remains a significant obstacle for the implementation of machine learning in scientific applications. In particular, estimating how a system might…
Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem
Haruki Hirasawa, Sookyung Kim, Peetak Mitra +6
Marine cloud brightening (MCB) is a proposed climate intervention technology to partially offset greenhouse gas warming and possibly avoid crossing climate tipping points. The impa…
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
Feras A. Batarseh, Priya L. Donti, Ján Drgoňa +14
Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefor…
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
Varun Shankar, Gavin D. Portwood, Arvind T. Mohan +6
In fluid physics, data-driven models to enhance or accelerate solution methods are becoming increasingly popular for many application domains, such as alternatives to turbulence cl…
Improved Methods for Mixing-Limited Spray Modeling
Majid Haghshenas, Peetak P. Mitra, Chu Wang +3
The realization that interfacial features play little role in diesel spray vaporization and advection has changed the modus operandi for spray modeling. Lagrangian particle trackin…
The Eulerian Lagrangian Mixing-Oriented (ELMO) Model
David P. Schmidt, Majid Haghshenas, Peetak P. Mitra +4
Past Lagrangian/Eulerian modeling has served as a poor match for the mixing limited physics present in many sprays. Though these Lagrangian/Eulerian methods are popular for their l…
Network Compression for Machine-Learnt Fluid Simulations
Peetak Mitra, Vaidehi Venkatesan, Nomit Jangid +7
Multi-scale, multi-fidelity numerical simulations form the pillar of scientific applications related to numerically modeling fluids. However, simulating the fluid behavior characte…
Turbulence forecasting via Neural ODE
Gavin D. Portwood, Peetak P. Mitra, Mateus Dias Ribeiro +9
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that ar…
Modeling PKT at a global level: A machine learning approach
Peetak Mitra, Suhrid Deshmukh
It is well-accepted that the ability to go from one place to another, or mobility, contributes significantly to one's wellbeing. The need for mobility is universal, but the demand…
Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework
Peetak Mitra
We propose a new deep learning based framework to identify pedestrians, and caution distracted drivers, in an effort to prevent the loss of life and property. This framework uses t…
Algorithm for estimating swirl angles in multi-intake hydraulic sumps
Peetak Mitra, Niranjan Gudibande, Kannan Iyer +1
The paper has been withdrawn effective November 18, 2015. Hydraulic Pump sumps are designed to provide a swirl free flow to the pump. The degree of swirl is measured in physical mo…