activity
20192026
most citedA Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction

10 citations · 12 across the 8 of their papers we have counts for

collaborators

9 papers

physics.plasm-ph2026

Hybrid collisional-radiative modeling for high-fidelity atomic kinetics

Prashant Sharma, Christopher J. Fontes, Mark Zammit +3

The fidelity of collisional-radiative (CR) models is critical for advancing our understanding of radiative properties and ionization balance in fusion plasmas. In this work, we pre…

physics.plasm-ph2025

A Runaway Electron Avalanche Surrogate for Partially Ionized Plasmas

Jonathan S. Arnaud, Xian-Zhu Tang, Christopher J. McDevitt

A physics-constrained deep learning surrogate that predicts the exponential ``avalanche'' growth rate of runaway electrons (REs) for a plasma containing partially ionized impuritie…

physics.plasm-ph2025

Aligning Thermal and Current Quenches with a High Density Low-Z Injection

Jason Hamilton, Luis Chacon, Giannis Keramidas +1

The conventional approach for thermal quench mitigation in a tokamak disruption is through a high-Z impurity injection that radiates away the plasma's thermal energy before it reac…

stat.ML2025

LiLaN: A Linear Latent Network as the Solution Operator for Real-Time Solutions to Stiff Nonlinear Ordinary Differential Equations

William Cole Nockolds, C. G. Krishnanunni, Tan Bui-Thanh +1

Solving stiff ordinary differential equations (StODEs) requires sophisticated numerical solvers, which are often computationally expensive. In general, traditional explicit time in…

physics.plasm-ph2024★ 1 cited

An Efficient Surrogate Model of Secondary Electron Formation and Evolution

Christopher J. McDevitt, Jonathan Arnaud, Xian-Zhu Tang

This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [C. McDevitt et al., A physics-constrained deep learning treatment of ru…

physics.plasm-ph2024

A Physics-Constrained Deep Learning Treatment of Runaway Electron Dynamics

Christopher J. McDevitt, Jonathan Arnaud, Xian-Zhu Tang

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A dist…