Plasma physicist

Dragoș Iustin Palade

Research at INFLPR (Bucharest/Măgurele) on turbulent transport in fusion plasmas, quantum plasmas, and selective applications of AI to modeling and diagnostics.

Head of Plasma Theory Group
Scientific Researcher III

CV (PDF) Publications (PDF) Google Scholar Email

About

Physics (BSc, MSc, PhD) — University of Bucharest.
PhD (2019, summa cum laude) on Applications of transport models in the study of quantum plasmas.
At INFLPR since 2013; research focus transitioned from many-body quantum systems to turbulent transport in fusion plasmas.

Role

Head of Plasma Theory Group (since 2020)
Scientific Researcher III (since 2021).

EUROfusion

Team member in the WPMST1 and WPTE packages.

transport turbulence tokamaks numerical codes

Other roles

Principal Investigator (PI) of “Machine learning tools for the prediction of turbulent transport in fusion plasmas” , a UEFISCDI -funded research grant (see Projects / Resources).

Research

Turbulent transport in fusion plasmas

Modeling and characterization of transport regimes and scalings in magnetized plasmas.
Connecting reduced models with numerical/experimental constraints.

Relevant papers (PDF) WPTE

Quantum plasmas & many-body transport

Transport models applied to quantum systems (foundations from earlier work).
Bridging analytic treatments with tractable reduced descriptions.

Background (CV)

AI / data-driven tools (selective)

Applications of machine learning where it adds real leverage (e.g., surrogate modeling, pattern discovery).
Focus on interpretability and physics constraints.

Group / collaboration

Open to seminar invitations, collaboration proposals, and student co-supervision where aligned.

Get in touch

Publications

For a full list, see the PDF or Google Scholar.

T3ST code: turbulent transport in tokamaks via stochastic trajectories — D.I. Palade and L.M. Pomârjanschi 2025 Nucl. Fusion 65 086007. on the home-lab T3ST code.
Predicting the turbulent transport of cosmic rays via neural networks — D.I. Palade JCAP01(2024)002. on the use of Neural Networks for turbulent transport prediction.
Peaking and hollowness of low-Z impurity profiles: an interplay between ITG and TEM induced turbulent transport — D.I. Palade 2023 Nucl. Fusion 63 046007. On the effect of turbulence type on pinch direction.
Nonlocal orbital-free kinetic pressure tensors for the Fermi gas — D.I. Palade 2018 Phys. Rev. B 98, 245401. On the kinetic energy functions for quantum systems consistent with the Lindhard function limit.