Sankaran Mahadevan
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View article: Operational risk quantification of power grids using graph neural network surrogates of the DC optimal power flow
Operational risk quantification of power grids using graph neural network surrogates of the DC optimal power flow Open
View article: Reliability engineering, risk management, and trustworthiness assurance for AI systems
Reliability engineering, risk management, and trustworthiness assurance for AI systems Open
View article: Global Sensitivity Analysis for Microstructural Features to Variability in Elemental Concentration of Additively Manufactured Alloy 718
Global Sensitivity Analysis for Microstructural Features to Variability in Elemental Concentration of Additively Manufactured Alloy 718 Open
View article: Global Sensitivity Analysis for Microstructural Features to Variability in Elemental Concentration of Additively Manufactured Alloy 718
Global Sensitivity Analysis for Microstructural Features to Variability in Elemental Concentration of Additively Manufactured Alloy 718 Open
View article: Human-Centered Design and Iterative Refinement of Tools and Methods to Implement a Surveillance and Risk Prediction System for Clinical Deterioration in Ambulatory Cancer Care
Human-Centered Design and Iterative Refinement of Tools and Methods to Implement a Surveillance and Risk Prediction System for Clinical Deterioration in Ambulatory Cancer Care Open
Background A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in medically complex patients, to mitigate the effects of adverse event…
View article: Graph neural networks for power grid operational risk assessment under evolving unit commitment
Graph neural networks for power grid operational risk assessment under evolving unit commitment Open
View article: Active learning for adaptive surrogate model improvement in high-dimensional problems
Active learning for adaptive surrogate model improvement in high-dimensional problems Open
View article: Graph neural networks for power grid operational risk assessment under evolving grid topology
Graph neural networks for power grid operational risk assessment under evolving grid topology Open
This article investigates the ability of graph neural networks (GNNs) to identify risky conditions in a power grid over the subsequent few hours, without explicit, high-resolution information regarding future generator on/off status (grid …
View article: Dependence structure learning and joint probabilistic forecasting of stochastic power grid variables
Dependence structure learning and joint probabilistic forecasting of stochastic power grid variables Open
View article: Power grid operational risk assessment using graph neural network surrogates
Power grid operational risk assessment using graph neural network surrogates Open
We investigate the utility of graph neural networks (GNNs) as proxies of power grid operational decision-making algorithms (optimal power flow (OPF) and security-constrained unit commitment (SCUC)) to enable rigorous quantification of the …
View article: Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
Operational risk quantification of power grids using graph neural network surrogates of the DC OPF Open
A DC OPF surrogate modeling framework is developed for Monte Carlo (MC) sampling-based risk quantification in power grid operation. MC simulation necessitates solving a large number of DC OPF problems corresponding to the samples of stocha…
View article: Novel Approaches and Technologies for Aging Management
Novel Approaches and Technologies for Aging Management Open
As part of the application process for license renewal and subsequent license renewal, nuclear utilities must perform an evaluation to confirm that they have appropriately considered aging effects on plant structures, systems, components w…
View article: An Extension to the Predictive Capability Maturity Model to Assess Model Credibility Using Dempster-Shafer Theory
An Extension to the Predictive Capability Maturity Model to Assess Model Credibility Using Dempster-Shafer Theory Open
View article: Correction: A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
Correction: A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives Open
View article: Editorial: Uncertainty quantification in characterization, modelling, and design of materials
Editorial: Uncertainty quantification in characterization, modelling, and design of materials Open
EDITORIAL article Front. Mater., 14 December 2022Sec. Computational Materials Science Volume 9 - 2022 | https://doi.org/10.3389/fmats.2022.1111499
View article: A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives Open
View article: Reliability and risk metrics to assess operational adequacy and flexibility of power grids
Reliability and risk metrics to assess operational adequacy and flexibility of power grids Open
View article: A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies Open
View article: Probabilistic physics-informed machine learning for dynamic systems
Probabilistic physics-informed machine learning for dynamic systems Open
View article: Model Form Error Correction in Computational Simulations.
Model Form Error Correction in Computational Simulations. Open
View article: Just-In-Time Learning for Operational Risk Assessment in Power Grids
Just-In-Time Learning for Operational Risk Assessment in Power Grids Open
In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation. The computational requirements of the forward uncerta…
View article: Non-intrusive estimation of model error and discrepancy in dynamics models
Non-intrusive estimation of model error and discrepancy in dynamics models Open
View article: A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives Open
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by c…
View article: A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies Open
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by c…
View article: Special Issue on Uncertainty Quantification and Management in Additive Manufacturing
Special Issue on Uncertainty Quantification and Management in Additive Manufacturing Open
Additive manufacturing (AM) represents a new paradigm of manufacturing where parts are manufactured using their 3D models (such as CAD models) by joining materials in a layer-by-layer manner. Widespread implementation of additive manufactu…
View article: Variance-based sensitivity analysis of dynamic systems with both input and model uncertainty
Variance-based sensitivity analysis of dynamic systems with both input and model uncertainty Open
View article: ROBUST IMPORTANCE SAMPLING FOR BAYESIAN MODEL CALIBRATION WITH SPATIOTEMPORAL DATA
ROBUST IMPORTANCE SAMPLING FOR BAYESIAN MODEL CALIBRATION WITH SPATIOTEMPORAL DATA Open
This paper addresses two challenges in Bayesian calibration: 1) computational speed of existing sampling algorithms, and 2) calibration with spatio-temporal responses. The commonly used Markov Chain Monte Carlo (MCMC) approaches require ma…
View article: Optimal Selection of Calibration and Validation Test Samples under Uncertainty.
Optimal Selection of Calibration and Validation Test Samples under Uncertainty. Open
View article: Big Data Analytics in Online Structural Health Monitoring
Big Data Analytics in Online Structural Health Monitoring Open
This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly hetero…
View article: Mission-based reliability prediction in component-based systems
Mission-based reliability prediction in component-based systems Open
This paper develops a framework for the extraction of a reliability block diagram in component-based systems for reliability prediction with respect to specific missions. A mission is defined as a composition of several high-level function…