13 citations
- Torino e-districtIT2 papers
- University of MichiganUS2 papers
- Amazon (United States)US1 paper
- ARC Centre of Excellence for Gravitational Wave DiscoveryAU1 paper
- Astronomical ObservatoryRS1 paper
- Astronomical Observatory of CapodimonteIT1 paper
- Australian Regenerative Medicine InstituteAU1 paper
- Brera Astronomical ObservatoryIT1 paper
- CatalystGB1 paper
- Center for Astrophysics Harvard & SmithsonianUS1 paper
- Centra HealthUS1 paper
- Cosmos Corporation (United States)US1 paper
10 papers
A Faint Progenitor System for the Faint Supernova 2024vjm
Erez A. Zimmerman, Avishay Gal-Yam, Paul J. Groot +68
Type Ia Supernovae (SNe Ia) are well known for their role as standardizable cosmological candles. Their uniformity is credited to their single origin as thermonuclear explosions of…
Real-Time Procedural Learning From Experience for AI Agents
Dasheng Bi, Yubin Hu, Mohammed N. Nasir
Learning how to do things from trial and error in real time is a hallmark of biological intelligence, yet most LLM-based agents lack mechanisms to acquire procedural knowledge afte…
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…