Subhamoy Sen

Structural parameter identification with hybrid physics informed neural network

Nikhil Mahar, Gajendra Yadav, Kajal Thakur, Subhamoy Sen et autres

System identification (SI) is critical for ensuring the reliability of structural and mechanical components across engineering applications. Traditional model-based SI methods often struggle with complex dynamics and the scarcity of accurate physical models, while purely data-driven, model-free approaches though simple and fast …

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Cycle-Consistent Framework for Mooring Tension Prediction in Floating Offshore Wind Turbines

Kajal Thakur, Nikhil Mahar, Gajendra Yadav, Subhamoy Sen

The accurate prediction of mooring and structural tension is essential for efficient structural health monitoring (SHM) of floating offshore wind turbines (FOWTs), which are exposed to extreme harsh and dynamic sea conditions. This paper presents a data-driven framework inspired by the Deep …

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Structural Representation Learning for Thermal Turbulence Detection in Infrared Imagery using YOLO

Akash Deep, Subhamoy Sen, Arvind Keprate

Thermal turbulence degrades imaging performance in long-range infrared systems by introducing spatially varying distortions that appear as irregular intensity fluctuations and curvilinear patterns. Detecting these regions is challenging due to the absence of well-defined boundaries and their diffuse nature. This work investigates …

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Uncertainty-Aware Surrogate for Fatigue Assessment of Moorings in Offshore Wind Turbines

Rohit Kumar, Arvind Keprate, Subhamoy Sen

Floating Offshore Wind Turbines (FOWTs) deployed in deep waters extract wind energy via floating platforms that are station kept by mooring lines subjected to complex, highly dynamic loading conditions. The cyclic nature of these dynamic loads induces fatigue damage in the mooring …

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Time-Dependent Reliability Assessment of Mass Concrete Structures: Leveraging an LSTM-Based Surrogate Model for Early-Age Thermal Cracking under Parametric Uncertainty

Maninder Pal Singh, Subhamoy Sen

Mass concrete structures encounter a significant challenge with heat dissipation during hydration, often leading to thermal cracks due to nonuniform temperature distribution. An accurate reliability estimate concerning thermal cracking is essential to mitigate the risks of failure. Since concrete gains strength over …

in (code pays fourni par la source)

Corrosion estimation in floating offshore wind turbine mooring with particle filter coupled with DeepONet predictor

Rohit Kumar, Arvind Keprate, Subhamoy Sen

Mooring lines of floating offshore wind turbines (FOWTs) are continuously subjected to harsh environmental loading, making them highly susceptible to fatigue damage, while corrosion further accelerates degradation and may cause premature failure. Conventional monitoring methods rely on underwater sensors, which are costly …

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Compliant Modular Tensegrity Robotic System with Actuated X-shaped Members

David Herrmann, Lukas Lehmann, Leon Schaeffer, Felix Pink et autres

This paper presents a modular tensegrity-based robotic system with fully integrated actuation inside each compressed member, eliminating external cables and base structures. The design decouples actuation from prestress, enabling precise motion control while preserving high structural compliance and robustness. Experimental investigations demonstrate …

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Compliant Modular Tensegrity Manipulator Enabling Seahorse-Like Spiral Grasping

Lukas Lehmann, David Herrmann, Leon Schaeffer, Kilian Mittnacht et autres

This paper presents a compliant, modular, tendondriven manipulator based on tensegrity joints. The design and motion are inspired by the structure and spiral-like grasping behavior of the seahorse tail. The characteristic prestress of the tensegrity-structure significantly influences its gripping capabilities and can …

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Implicit Runge Kutta physics informed neural network for parameter identification of structural systems

Nikhil Mahar, Subhamoy Sen, Laurent Mevel

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Bayesian inversion for parameter estimation of advanced soil constitutive model and associated uncertainty quantification: Transitional Markov Chain Monte Carlo versus Particle Filtering

Km Shraddha, Subhamoy Sen, Shashank shekhar pathak, Mousumi Mukherjee

Precise estimation and uncertainty quantification of parameters of advanced soil models become crucial for reliable constitutive predictions and risk-informed design of geo-structures. Bayesian updating offers a robust approach for addressing model and measurement uncertainties while yielding probabilistic parameter estimates. Moreover, it circumvents …

in (code pays fourni par la source)

Bayesian Physics-Informed Neural Network for Parameter Estimation of Mooring Line

Nikhil Mahar, Subhamoy Sen, Laurent Mevel

in, fr (code pays fourni par la source)

A Comparative Study on Estimation of Soil Constitutive Model Parameter Employing Sequential Monte Carlo and Markov Chain Monte Carlo Techniques

Km Shraddha, Subhamoy Sen, Shashank shekhar pathak, Mousumi Mukherjee

in (code pays fourni par la source)