arXiv subjects

Gaurav Chopra

Publications and source records attributed to Gaurav Chopra.

11 recordsLinked to original sources

arXiv2026

A Graph Neural Network Framework for Characterizing Rainfall Variability Regimes across India

The Indian Summer Monsoon shows significant spatial variation. While prior work primarily focused on forecasting rainfall amounts, little attention has been given to how consistently a location's seasonal rainfall trajectory repeats from year to year. We introduce a graph-based machine learning framework to classify locations across India by this inter-annual consistency. Using 2001 to 2022 GSMaP ISRO data (excluding 2012), we constructed graphs for 29,026 grid points where nodes represent individual years and edges denote cosine similarity. A Graph Convolutional Network classified locations as either consistent or erratic with 96.8% accuracy. Applied to the Indian landmass, the model successfully identified the Western Ghats, Northeast India, and parts of central India as consistent regions. This classification was rigorously validated through statistical testing and temporal stability analysis, showing 93.6% agreement across two independent timeframes. Crucially, the results reveal a previously unreported coupling: regions with higher rainfall volumes are also the most temporally repeatable year-to-year, demonstrating an emergent, spatially coherent structure.

physics.ao-ph↗

arXiv2026

Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon

Advancements in remote sensing have led to development of several satellite-derived precipitation products; however, their accuracy must be evaluated before use in scientific and operational studies. This study comprehensively assesses six widely used datasets PERSIANN CCS, CHIRPS, MSWEP, IMERG, AgERA5, and GSMaP ISRO against a dense rain gauge network across Karnataka, a southern Indian state characterized by diverse climatic conditions and complex topography. The analysis focuses on the Indian summer monsoon season for 2011 to 2022. To complement traditional metrics, tools from complex network theory were applied to investigate spatial organization and connectivity patterns of rainfall. A functional climate network approach was used to construct rainfall correlation networks, while event synchronization, a nonlinear measure, quantified the co occurrence of extreme events. Most products reproduced large scale monsoon features, yet their ability to represent intensity categories and extremes varied. GSMaP ISRO showed the highest correlation, lowest bias, and RMSE across subregions, whereas PERSIANN CCS exhibited systematic errors, particularly in Western Ghats, though correlations improved over interior plains. Network-based analysis reaffirmed GSMaP ISROs skill in replicating spatial correlation structures, capturing high coherence in regions dominated by large-scale processes and lower coherence in areas influenced by localized dynamics. The observed rainfall network revealed strong synchronization between the coastal region and central Karnataka, indicating broad spatial co occurrence of extremes, while the Malnad region showed weaker connectivity, suggesting localized events. GSMaP ISRO closely reproduced this degree distribution, reflecting corrections using IMD gridded dataset. Future work should improve sub-daily and localized rainfall estimates, especially in complex terrain.

physics.ao-ph↗

arXiv2026

Resolving the Paradox of Changing El Niño-Monsoon Relation through Synchronization of Chaotic Oscillators

For over a century, the relationship between Indian summer monsoon rainfall and El Nino-Southern Oscillation has been the foundation of 'long-range' prediction of Indian monsoon. This relation is estimated from correlations between Indian summer monsoon rainfall and a Pacific sea surface temperature-based index of El Nino-Southern Oscillation. However, a prominent multi-decadal variability in the correlation raises doubts on the realism of El Nino-Monsoon relation and stability of Indian monsoon predictability. Previous studies discussed that Pacific-based El Nino-Southern Oscillation indices do not represent El Nino's global influence completely, making their correlation with Indian monsoon unreliable. To address this limitation, a Global El Nino-Southern Oscillation framework based on the depth of the 20 degree Celsius isotherm is developed, integrating subsurface signal from all three tropical ocean basins and maximizing Indian monsoon teleconnections. Contrary to previous findings, the 20 degree Celsius isotherm-based Global El Nino-Southern Oscillation exhibits a strong and stable correlation (greater than 0.8) with Indian monsoon at an 18-month lead. Through a re-examination of the El Nino-Monsoon relationship with the superior Global El Nino-Southern Oscillation predictor, we show that the true relationship is independent of global warming and stationary in time. We discover that the stationarity in the El Nino-Monsoon relationship emerges as a natural consequence of chaotic synchronization between Indian summer monsoon rainfall and 20 degree Celsius isotherm at an 18-month lead. Such synchronization between chaotic climate variables provides a new physical basis for climate predictability beyond the conventional deterministic limit set by chaos. Our findings provide the foundation and renewed confidence in long range climate prediction.

physics.ao-ph↗

arXiv2026

Prediction and Predictability of the Wet-Season Rainfall over Southeast India

The challenge in predicting sub-regional climate within the Indian monsoon region is exacerbated by its increasing variability in a warming world. While exploring the seasonal predictability of rainfall over the state of Tamil Nadu in southeast India, we identify an overall increase in the monthly rainfall and its variability in recent years due to an increase in surface temperature, water vapour and moisture convergence. We attribute the increasing excess rainfall to a long-term reduction in convective inhibition. We further find an increasing trend in the length of the rainy season due to an earlier onset and a delayed withdrawal of the large-scale monsoon over the southeastern and southwestern regions of southern peninsular India, respectively. Further, the simultaneous (0- month lead) predictability of the primary wet-season (October-December, OND) rainfall over Tamil Nadu is dominated by sea surface temperature (SST) anomalies in the North Indian Ocean. However, a global tropical SST climate network reveals a high potential predictability and potential to realize significant forecast skill at a lead time of up to 10 months. The long-lead predictability arises from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. Our findings provide a robust data-driven methodology for skillful seasonal rainfall prediction over Tamil Nadu, despite the increasing rainfall variability.

physics.ao-ph↗

arXiv2025

A network-theoretic approach for characterizing Mack-mode instability in high-speed boundary layers

Here we present a network theory-based approach to investigate the Mack-mode instability signature found in high-speed schlieren data from a Mach 6 laminar boundary layer flow over a $7^\circ$ cone. The data contain instability wave packets in the form of coherent rope-like structures which exhibit intermittency. The intermittency implies that conventional Fourier techniques are not particularly well suited for analysis. Network analysis, which is well known for handling episodic spatio-temporal data in a variety of complex systems, provides an alternate and more suitable framework. Techniques from time-varying spatial proximity networks are applied to the present data. The connected components in the network topology reveal lines of constant phase for coherent wave packets associated with the instability, and localized regions of high schlieren light intensity for intermittent laminar or turbulent flow states. The orientation angle of the connected network components is found to be a suitable metric for identifying components associated with the Mack-mode instability, and that enables detailed characterization of the wavelength and propagation speeds of the instability wave packets. Beyond the characterization exercise, network analysis can provide a powerful framework for understanding the fundamental nature of intermittency and its role in the laminar-to-turbulent flow transition process.

physics.flu-dyn↗

arXiv2025

State and Memory is All You Need for Robust and Reliable AI Agents

Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, planning, and tool integration. Here, we introduce SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution. Agents are constructed dynamically from source code documentation and augmented with finite-state automata (FSA) memory, enabling persistent state tracking and context-aware decision-making. This approach eliminates the need for manual prompt engineering and allows for robust, scalable deployment across diverse applications via maintaining context across extended workflows and to recover from tool or execution failures. We validate SciBORG through integration with both physical and virtual hardware, such as microwave synthesizers for executing user-specified reactions, with context-aware decision making and demonstrate its use in autonomous multi-step bioassay retrieval from the PubChem database utilizing multi-step planning, reasoning, agent-to-agent communication and coordination for execution of exploratory tasks. Systematic benchmarking shows that SciBORG agents achieve reliable execution, adaptive planning, and interpretable state transitions. Our results show that memory and state awareness are critical enablers of agentic planning and reliability, offering a generalizable foundation for deploying AI agents in complex environments.

cs.MA↗

arXiv2025

Climatic Phase Transitions Unravel the Onset and Withdrawal of Indian Monsoon

The livelihood and food security of more than a billion people depend on the Indian monsoon (IM). Yet, a universal definition of the large-scale season and progress of IM is missing. Even though IM is a planetary-scale convectively coupled system arising largely from seasonal migration of the Intertropical Convergence Zone (ITCZ), the definitions of its onset and progression are based on local weather observations, making them practically inutile due to the detection of bogus onsets. Using climate networks, we show that small-scale clusters of locally defined rainfall onsets coalesce through two abrupt climatic phase transitions defining large-scale monsoon onsets over Northeast India and the Indian peninsula, respectively. These abrupt transitions are interspersed with continuous growth of clusters. Breaking the conventional wisdom that IM starts from southern peninsula and expands northward and westward, we unveil that IM starts from Northeast India and expands westward and northward, covering the entire country. We show that the large-scale monsoon onset over the Indian peninsula is critically dependent on the characteristics of monsoon onset over Northeast India. Unlike existing definitions, a rapid and consistent northward propagation of rainfall establishing the ITCZ manifests after our network-based onset dates. Thus, our definition captures the IM onset better than the existing definitions.

physics.ao-ph↗

arXiv2024

Cross-variable amplitude-frequency coupling during intermittency in a turbulent thermoacoustic system

We investigate flame-acoustic interactions in a turbulent combustor during the state of intermittency before the onset of thermoacoustic instability using complex networks. Experiments are performed in a turbulent bluff-body stabilized dump combustor where the inlet airflow rate is varied quasi-statically and continuously. We construct a natural visibility graph from the local heat release rate fluctuations at each location. Comparing the average degree during epochs of high and low amplitude acoustic pressure oscillations during the state of intermittency, we detect frequency modulation in local heat release rate signals. Through this approach, we discover unique spatial patterns of cross-variable coupling between the frequency of heat release rate fluctuations and the amplitude of acoustic pressure fluctuations. The frequency of heat release rate lfuctuations increases in regions of flame anchoring owing to high-frequency excitation of the flow and flame during epochs of high-amplitude acoustic pressure dynamics. On the other hand, the frequency of heat release rate fluctuations decreases in regions associated with flame front distortions by large coherent vortices. In experiments with continuously varying airflow rates, the spatial pattern of frequency modulation varies with an increase in the average amplitude of acoustic pressure fluctuations owing to an increase in the epochs of periodic acoustic pressure dynamics and the size of vortices forming in the flow. Dynamic shifts in the location of flame anchoring induce low-frequency fluctuations in heat release rate fluctuations during very high-amplitude intermittent acoustic pressure dynamics. Our approach using conditional natural visibility graphs thus reveals the spatial pattern of amplitude-frequency coupling between the co-evolving flame and the acoustic field dynamics in turbulent reacting flows.

physics.flu-dyn↗

arXiv2024

Comprehensive Lipidomic Automation Workflow using Large Language Models

Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.

q-bio.QM↗

arXiv2024

Paddy: Evolutionary Optimization Algorithm for Chemical Systems and Spaces

Optimization of chemical systems and processes have been enhanced and enabled by the guidance of algorithms and analytical approaches. While many methods will systematically investigate how underlying variables govern a given outcome, there is often a substantial number of experiments needed to accurately model these relations. As chemical systems increase in complexity, inexhaustive processes must propose experiments that efficiently optimize the underlying objective, while ideally avoiding convergence on unsatisfactory local minima. We have developed the Paddy software package around the Paddy Field Algorithm, a biologically inspired evolutionary optimization algorithm that propagates parameters without direct inference of the underlying objective function. Benchmarked against the Tree of Parzen Estimator, a Bayesian algorithm implemented in the Hyperopt software Library, Paddy displays efficient optimization with lower runtime, and avoidance of early convergence. Herein we report these findings for the cases of: global optimization of a two-dimensional bimodal distribution, interpolation of an irregular sinusoidal function, hyperparameter optimization of an artificial neural network tasked with classification of solvent for reaction components, and targeted molecule generation via optimization of input vectors for a decoder network. We anticipate that the facile nature of Paddy will serve to aid in automated experimentation, where minimization of investigative trials and or diversity of suitable solutions is of high priority.

math.OC↗

arXiv2024

Evolution of clusters of turbulent reattachment due to shear layer instability in flow past a circular cylinder

We perform large eddy simulations of flow past a circular cylinder for the Reynolds number ($Re$) range, $2\times 10^3 \leq Re \leq 4\times10^5$, spanning subcritical, critical and supercritical regimes. We investigate the spanwise coherence of the flow in the critical and supercritical regimes using complex networks. In these regimes, the separated flow reattaches to the surface in a turbulent state due to the turbulence generated by the shear layer instability. In the early critical regime, the turbulent reattachment does not occur simultaneously at all span locations. It occurs incoherently along the span in clusters. We treat strong surface pressure fluctuations due to the shear layer instability as extreme events and construct time-varying spatial proximity networks where links are based on synchronization between events. This analysis unravels the underlying complex spatio-temporal dynamics by enabling the estimation of characteristics of clusters of turbulent reattachment via the concept of connected components. In the critical regime, the number and size of the clusters increase with increase in $Re$. At higher $Re$ in the supercritical regime, they coalesce to form bigger clusters, resulting in increase in spanwise coherence of turbulent reattachment. We find that the size and number of clusters govern the variation of the time-averaged coefficient of drag ($\overline{C}_D$) in the critical and supercritical regimes. $\overline{C}_D$ exhibits power-law distribution with the most probable cluster size ($\overline{C}_D \propto E(S_C)^{-\frac{2}{5}}$) and the largest cluster size ($\overline{C}_D \propto {\overline{S}_{CL}}^{-\frac{2}{5}}$).

physics.flu-dyn↗