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20192025
most citedPrediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

558 citations · 585 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CE2025

An Agentic AI Workflow to Simplify Parameter Estimation of Complex Differential Equation Systems

Saakaar Bhatnagar

Parameter identification for mechanistic Ordinary Differential Equation (ODE) models underpins prediction and control in several applications, yet remains a manual and labor-intens…

cs.CE2024★ 1 cited

A Layered Swarm Optimization Method for Fitting Battery Thermal Runaway Models to Accelerating Rate Calorimetry Data

Saakaar Bhatnagar, Andrew Comerford, Zelu Xu +4

Thermal runaway in lithium-ion batteries is a critical safety concern for the battery industry due to its potential to cause uncontrolled temperature rises and subsequent fires tha…

cs.CE2024★ 13 cited

Chemical Reaction Neural Networks for Fitting Accelerating Rate Calorimetry Data

Saakaar Bhatnagar, Andrew Comerford, Zelu Xu +3

As the demand for lithium-ion batteries rapidly increases there is a need to design these cells in a safe manner to mitigate thermal runaway. Thermal runaway in batteries leads to…

cs.CE2024★ 1 cited

Investigating the Surrogate Modeling Capabilities of Continuous Time Echo State Networks

Saakaar Bhatnagar

Continuous Time Echo State Networks (CTESNs) are a promising yet under-explored surrogate modeling technique for dynamical systems, particularly those governed by stiff Ordinary Di…

cs.CE2023★ 12 cited

Physics Informed Neural Networks for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data

Saakaar Bhatnagar, Andrew Comerford, Araz Banaeizadeh

Successfully training Physics Informed Neural Networks (PINNs) for highly nonlinear PDEs on complex 3D domains remains a challenging task. In this paper, PINNs are employed to solv…