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Researcher profile

Vipin Kumar

Vipin Kumar contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine Learningastro-ph.GAastro-ph.SReess.SPArtificial Intelligenceastro-ph.EPastro-ph.IMComputer VisionInformation Retrievalmath.NAmath.OCNumerical Analysisphysics.comp-ph

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Published work

9 published item(s)

preprint2026arXiv

To Use AI as Dice of Possibilities with Timing Computation

The dominant noun-based modeling paradigm has fundamentally constrained AI development, precluding any adequate representation of the future as an open temporal dimension. This paper introduces a verb-based paradigm, together with precise definitions of \emph{timing computation} and \emph{possibility}, that enables AI to function as an effective instrument for realizing the grammar of our thought. Applied to longitudinal EHR data from 3,276 breast cancer patients, the framework empirically demonstrates: (1) automatic discovery of clinically significant patient trajectories, and (2) counterfactual timing deduction. Both results are purely data-driven, require no prior domain knowledge, and, to our knowledge, represent the first such demonstrations in the machine learning literature.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This paper provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Investigating star-formation activity towards the southern HII region RCW 42

The star-forming activity in the HII region RCW 42 is investigated using multiple wavebands, from near-infrared to radio wavelengths. Located at a distance of 5.8 kpc, this southern region has a bolometric luminosity of 1.8 $\times$ 10$^6$ L$_{\odot}$. The ionized gas emission has been imaged at low radio frequencies of 610 and 1280 MHz using the Giant Metrewave Radio Telescope, India and shows a large expanse of the HII region, spanning $20\times 15$ pc$^2$. The average electron number density in the region is estimated to be $\sim70$ cm$^{-3}$, which suggests an average ionization fraction of the cloud to be $11\%$. An extended green object EGO G274.0649-01.1460 and several young stellar objects have been identified in the region using data from the 2MASS and Spitzer surveys. The dust emission from the associated molecular cloud is probed using Herschel Space Telescope, which reveals the presence of 5 clumps, C1-C5, in this region. Two millimetre emission cores of masses 380 and 390 M$_{\odot}$ towards the radio emission peak have been identified towards C1 from the ALMA map at 1.4 mm. The clumps are investigated for their evolutionary stages based on association with various star-formation tracers, and we find that all the clumps are in active/evolved stage.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology

Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, building accurate broad-scale models for streamflow remains challenging in practice due to the variability in the dominant hydrologic processes, which are best captured by sets of process-related basin characteristics. Existing basin characteristics suffer from noise and uncertainty, among many other things, which adversely impact model performance. To tackle the above challenges, in this paper, we propose a novel Knowledge-guided Self-Supervised Learning (KGSSL) inverse framework to extract system characteristics from driver and response data. This first-of-its-kind framework achieves robust performance even when characteristics are corrupted. We show that KGSSL achieves state-of-the-art results for streamflow modeling for CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) which is a widely used hydrology benchmark dataset. Specifically, KGSSL outperforms other methods by up to 16 \% in reconstructing characteristics. Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Optical and near-infrared spectroscopy of Nova V2891 Cygni: evidence for shock-induced dust formation

We present multi-epoch optical and near-infrared observations of the highly reddened, \pion{Fe}{ii} class slow nova V2891 Cygni. The observations span 15 months since its discovery. The initial rapid brightening from quiescence, and the presence of a $\sim$35 day long pre-maximum halt, is well documented. The evidence that the current outburst of V2891 Cyg has undergone several distinct episodes of mass ejection is seen through time-varying P Cygni profiles of the O\,{\sc i} 7773\,$Å$ line. A highlight is the occurrence of a dust formation event centred around $\sim$+273d, which coincides with a phase of coronal line emission. The dust mass is found to be $\sim0.83-1.25 \times 10^{-10} M_{\odot}$. There is strong evidence to suggest that the coronal lines are created by shock heating rather than by photoionization. The simultaneous occurrence of the dust and coronal lines (with varying velocity shifts) supports the possibility that dust formation is shock-induced. Such a route for dust formation has not previously been seen in a nova, although the mechanism has been proposed for dust formation in some core-collapse supernovae. Analysis of the coronal lines indicates a gas mass and temperature of 8.35--8.42$\times10^{-7}$ M$_\odot$ and $\sim(4.8-9.1)\times10^{5}$~K respectively, and an overabundance of aluminium and silicon. A Case B analysis of the hydrogen lines yields a mass of the ionized gas of ($8.60\pm1.73)\times10^{-5}$ M$_{\odot}$. The reddening and distance to the nova are estimated to be $E(B-V)$ = 2.21$\pm$0.15 and $d$ = 5.50 kpc respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

First Results from MFOSC-P : Low Resolution Optical Spectroscopy of a Sample of M dwarfs within 100 parsecs

Mt. Abu Faint Object Spectrograph and Camera (MFOSC-P) is an in-house developed instrument for Physical Research Laboratory (PRL) 1.2m telescope at Mt. Abu India, commissioned in February 2019. Here we present the first science results derived from the low resolution spectroscopy program of a sample of M Dwarfs carried out during the commissioning run of MFOSC-P between February-June 2019. M dwarfs carry great significance for exoplanets searches in habitable zone and are among the promising candidates for the observatory&#39;s several ongoing observational campaigns. Determination of their accurate atmospheric properties and fundamental parameters is essential to constrain both their atmospheric and evolutionary models. In this study, we provide a low resolution (R$\sim$500) spectroscopic catalogue of 80 bright M dwarfs (J$<$10) and classify them using their optical spectra. We have also performed the spectral synthesis and $χ^2$ minimisation techniques to determine their fundamental parameters viz. effective temperature and surface gravity by comparing the observed spectra with the most recent BT-Settl synthetic spectra. Spectral type of M dwarfs in our sample ranges from M0 to M5. The derived effective temperature and surface gravity are ranging from 4000 K to 3000 K and 4.5 to 5.5 dex, respectively. In most of the cases, the derived spectral types are in good agreement with previously assigned photometric classification.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Inverse Problems, Deep Learning, and Symmetry Breaking

In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental difficulties for deploying the emerging end-to-end deep learning approach. Using the generalized phase retrieval problem as an illustrative example, we show that careful symmetry breaking on the training data can help get rid of the difficulties and significantly improve the learning performance. We also extract and highlight the underlying mathematical principle of the proposed solution, which is directly applicable to other inverse problems.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles

Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to simplified representations of the physical processes being modeled or challenges in selecting appropriate parameters. While-state-of-the-art machine learning models can sometimes outperform physics-based models given ample amount of training data, they can produce results that are physically inconsistent. This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws. An important aspect of our PGRNN approach lies in its ability to incorporate the knowledge encoded in physics-based models. This allows training the PGRNN model using very few true observed data while also ensuring high prediction accuracy. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where physics-based (also known as mechanistic) models are used, e.g., climate science, materials science, computational chemistry, and biomedicine.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data

Many real-world phenomena are observed at multiple resolutions. Predictive models designed to predict these phenomena typically consider different resolutions separately. This approach might be limiting in applications where predictions are desired at fine resolutions but available training data is scarce. In this paper, we propose classification algorithms that leverage supervision from coarser resolutions to help train models on finer resolutions. The different resolutions are modeled as different views of the data in a multi-view framework that exploits the complementarity of features across different views to improve models on both views. Unlike traditional multi-view learning problems, the key challenge in our case is that there is no one-to-one correspondence between instances across different views in our case, which requires explicit modeling of the correspondence of instances across resolutions. We propose to use the features of instances at different resolutions to learn the correspondence between instances across resolutions using an attention mechanism.Experiments on the real-world application of mapping urban areas using satellite observations and sentiment classification on text data show the effectiveness of the proposed methods.

0 citations0 reviewsNo code linkedOpen access