558 citations · 585 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…
Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks
Yaser Afshar, Saakaar Bhatnagar, Shaowu Pan +2
An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen fl…