arXiv subjects

Piyush Mehta

Publications and source records attributed to Piyush Mehta.

3 recordsLinked to original sources

arXiv2026

Reduced-Order Data Assimilation for Thermospheric Density Using Physics-informed SINDyc Models

Orbit prediction and space situational awareness require accurate thermospheric mass density, which responds nonlinearly to solar and geomagnetic forcing across several orders of magnitude. Physics-based general circulation models resolve that response but are computationally expensive, while empirical models run cheaply and carry no time-evolving atmospheric state. An autoregressive Sparse Identification of Nonlinear Dynamics with control (SINDyc-AR) model, derived from the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), captures the dominant modes of variability and their dependence on the drivers at a fraction of the parent model's cost. An extended Kalman filter assimilates in situ observations from CHAMP, GRACE, GRACE-FO, GOCE, and Swarm into that model across several orbital configurations and geomagnetic conditions, with a linear DMDc model as reference. Assimilation reduces density error relative to open-loop prediction, most visibly during geomagnetic storms and under single-satellite coverage. The two reduced-order models perform comparably throughout. NRLMSIS 2.1 and HASDM can outperform the assimilated model far from the assimilated track, so results are framed as improvements over the open-loop forecast. An observation-calibrated global density record spanning August 2000 to December 2025 is released alongside this work.

eess.SY↗

arXiv2023

Frequency Disentangled Features in Neural Image Compression

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 ability is indirectly under the effect of how close the relaxed quantization is to the actual hard quantization. Optimizing the parameters of a rate-distortion variational autoencoder (R-D VAE) is ruled by this approximated quantization scheme. In this paper, we propose a feature-level frequency disentanglement to help the relaxed scalar quantization achieve lower bit rates by guiding the high entropy latent features to include most of the low-frequency texture of the image. In addition, to strengthen the de-correlating power of the transformer-based analysis/synthesis transform, an augmented self-attention score calculation based on the Hadamard product is utilized during both encoding and decoding. Channel-wise autoregressive entropy modeling takes advantage of the proposed frequency separation as it inherently directs high-informational low-frequency channels to the first chunks and conditions the future chunks on it. The proposed network not only outperforms hand-engineered codecs, but also neural network-based codecs built on computation-heavy spatially autoregressive entropy models.

eess.IV↗

arXiv2022

Understanding variability in HASDM to support space traffic management

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 of unintentional collisions between objects due to accumulating space debris. Effective space traffic management needs accurate knowledge of the variability in upper atmosphere densities. Data assimilative modeling, where physics-based models are informed by measurements, supplies the best capability today for specifying and predicting space weather. The foundation for this modeling comes from the SET High Accuracy Satellite Drag Model (HASDM) density database. We report on studies to understand the variabilities in HASDM. We identify two thermospheric features from the SET HASDM density database. First, we have confirmed that the time scale is very rapid (1-hour) for molecular conduction above 200 km to transfer energy vertically in the thermosphere. This results couples with a longer timescale for conduction in the 100-200 km region where it takes up to 2 days for energy to transition across that region via molecular conduction. We now have an excellent picture of the timescales of energy change throughout the thermosphere. Second, the SET HASDM density data display a common range of variability despite the level of daily averaged geomagnetic activity as represented by Ap. During higher levels of daily averaged geomagnetic activity, the density mean and median values increase at all altitude levels. However, the relative range of variability is consistent from one daily average of Ap to the next. The reason is likely to be that the underlying pre-storm density of the thermosphere is determined by the solar EUV and FUV irradiances that create a thermal foundation of the upper atmosphere. The daily averaged geomagnetic activity is a perturbation upon that foundation.

physics.space-ph↗