50 citations · 263 across the 24 of their papers we have counts for
25 papers
Fast and Stable Nonlinear Emulation of TIEGCM Using an Autoregressive SINDy Framework
Daniele Sicoli, Sriram Narayanan, Piyush M. Mehta
The ever-growing number of satellites and resident space objects in low Earth orbit keeps increasing the risk of collision between these objects. To mitigate this risk, the develop…
Reduced-Order Data Assimilation for Thermospheric Density Using Physics-informed SINDyc Models
Sriram Narayanan, Daniele Sicoli, Piyush Mehta
Orbit prediction and space situational awareness require accurate thermospheric mass density, which responds nonlinearly to solar and geomagnetic forcing across several orders of m…
Spectral and Spatial Graph Learning for Multispectral Solar Image Compression
Prasiddha Siwakoti, Atefeh Khoshkhahtinat, Piyush M. Mehta +3
High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spati…
Neural-based Video Compression on Solar Dynamics Observatory Images
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +4
NASA's Solar Dynamics Observatory (SDO) mission collects extensive data to monitor the Sun's daily activity. In the realm of space mission design, data compression plays a crucial…
Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +1
While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression, their lack of inductive bias fo…
Neural-based Compression Scheme for Solar Image Data
Ali Zafari, Atefeh Khoshkhahtinat, Jeremy A. Grajeda +6
Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them eff…
Multi-spectral Entropy Constrained Neural Compression of Solar Imagery
Ali Zafari, Atefeh Khoshkhahtinat, Piyush M. Mehta +4
Missions studying the dynamic behaviour of the Sun are defined to capture multi-spectral images of the sun and transmit them to the ground station in a daily basis. To make transmi…
Multi-Context Dual Hyper-Prior Neural Image Compression
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +3
Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-bas…
Context-Aware Neural Video Compression on Solar Dynamics Observatory
Atefeh Khoshkhahtinat, Ali Zafari, Piyush M. Mehta +4
NASA's Solar Dynamics Observatory (SDO) mission collects large data volumes of the Sun's daily activity. Data compression is crucial for space missions to reduce data storage and v…
Frequency Disentangled Features in Neural Image Compression
Ali Zafari, Atefeh Khoshkhahtinat, Piyush Mehta +3
The design of a neural image compression network is governed by how well the entropy model matches the true distribution of the latent code. Apart from the model capacity, this abi…
Probabilistic Solar Proxy Forecasting with Neural Network Ensembles
Joshua D. Daniell, Piyush M. Mehta
Space weather indices are used commonly to drive forecasts of thermosphere density, which directly affects objects in low-Earth orbit (LEO) through atmospheric drag. One of the mos…
Reduced Order Probabilistic Emulation for Physics-Based Thermosphere Models
Richard J. Licata, Piyush M. Mehta
The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particul…
Advanced ensemble modeling method for space object state prediction accounting for uncertainty in atmospheric density
Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta
For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space situational a…
Stochastic modeling of physical drag coefficient -- its impact on orbit prediction and space traffic management
Smriti Nandan Paul, Phillip Logan Sheridan, Richard J. Licata +1
Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. I…
Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory
Ali Zafari, Atefeh Khoshkhahtinat, Piyush M. Mehta +4
NASA's Solar Dynamics Observatory (SDO) mission gathers 1.4 terabytes of data each day from its geosynchronous orbit in space. SDO data includes images of the Sun captured at diffe…
Understanding variability in HASDM to support space traffic management
W. Kent Tobiska, Marcin D. Pilinski, Shaylah Mutschler +5
With more commercial constellations planned, the number of Low Earth Orbit (LEO) objects is set to TRIPLE in two years. The growth in LEO objects directly increases the probability…
Calibrated and Enhanced NRLMSIS 2.0 Model with Uncertainty Quantification
Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer +2
The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research…
Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling
Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer +3
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators…
Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application
Richard J. Licata, Piyush M. Mehta
Machine learning (ML) has often been applied to space weather (SW) problems in recent years. SW originates from solar perturbations and is comprised of the resulting complex variat…
The current state and future directions of modeling thermosphere density enhancements during extreme magnetic storms
Denny M. Oliveira, Eftyhia Zesta, Piyush M. Mehta +4
Satellites, crewed spacecraft and stations in low-Earth orbit (LEO) are very sensitive to atmospheric drag. A satellite's lifetime and orbital tracking become increasingly inaccura…
Machine-Learned HASDM Model with Uncertainty Quantification
Richard J. Licata, Piyush M. Mehta, W. Kent Tobiska +1
The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Sp…
Benchmarking Forecasting Models for Space Weather Drivers
Richard J. Licata, W. Kent Tobiska, Piyush M. Mehta
Space weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serv…
Data-driven framework for real-time thermospheric density estimation
Piyush M. Mehta, Richard Linares
In this paper, we demonstrate a new data-driven framework for real-time neutral density estimation via model-data fusion in quasi-physical ionosphere-thermosphere models. The frame…
A new transformative framework for data assimilation and calibration of physical ionosphere-thermosphere models
Piyush M. Mehta, Richard Linares
Accurate specification and prediction of the ionosphere-thermosphere (IT) environment, driven by external forcing, is crucial to the space community. In this work, we present a new…