Artur Toshev

I'm a Founding Research Scientist at Zenithon AI, where I work across the machine learning stack, including representation learning, large-scale model training, and the data processing and curation pipelines behind them. My current work focuses on developing foundation models for plasma physics.

I'm also a final-year PhD candidate at Technical University of Munich, supervised by Professor Nikolaus Adams. My PhD research focuses on accelerating particle-based fluid dynamics with machine learning. In 2025, I completed research internships with the Meta FAIR Chemistry team in San Francisco and at the CAI-2 Division at Los Alamos National Laboratory.

A non-exhaustive list of what currently excites me includes: large-scale machine learning for scientific applications, representation learning for physical systems, geometric deep learning, latent-space generative modeling, turbulence and plasma physics. If any of these topics resonate with you, feel free to reach out.

More about me can be found in my resume. If you want to chat, just drop me an email or connect with me on one of the following platforms.

Email /  X  /  Google Scholar  /  Github  /  LinkedIn

profile photo
Publications

Enhancing Diffusion-Based Sampling with Molecular Collective Variables
J. Nam, B. Máté, A. P. Toshev, M. Kaniselvan, R. Gómez-Bombarelli, R. T. Q. Chen, B. Wood, G.-H. Liu, B. K. Miller
ICLR 2026
ICLR'26 / arXiv / code

LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities
F. Sestak, A. P. Toshev, A. Fürst, G. Klambauer, A. Mayr, J. Brandstetter
NeurIPS 2025
project page / tweets 1 and 2 / arXiv / code

Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
T. Prein, E. Pan, S. Haddouti, M. Lorenz, J. Jehkul, T. Wilk, C. Moran, M. P. Fotiadis, A. P. Toshev, E. Olivetti, J. L.M. Rupp
AI4Materials workshop at ICLR 2025
AI4Mat@ICLR'25 / poster

On Learning Quasi-Lagrangian Turbulence
A. P. Toshev, T. Kalinov, N. Gao, S. Günnemann, N. A. Adams
MLMP workshop at ICLR 2025
MLMP@ICLR'25 / poster

UPT++: Latent Point Set Neural Operators for Modeling System State Transitions
A. Fürst*, F. Sestak*, A. P. Toshev*, B. Alkin, N. A. Adams, A. Mayr, G. Klambauer, J. Brandstetter
MLMP workshop at ICLR 2025
MLMP@ICLR'25 / poster

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
A. P. Toshev, J. A. Erbesdobler, N. A. Adams, J. Brandstetter
ICML 2024
project page / tweet / ICML'24 / arXiv / poster / code

JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework
A. P. Toshev, H. Ramachandran, J. A. Erbesdobler, G. Galletti, J. Brandstetter, N. A. Adams
AI4DiffEq workshop at ICLR 2024
tweet / AI4DiffEq@ICLR'24 / arXiv / poster / code

LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
A. P. Toshev* , G. Galletti* , F. Fritz, S. Adami, N. A. Adams
NeurIPS 2023 Track on Datasets and Benchmarks
NeurIPS'23 / arXiv / poster / video / code

Accelerating Molecular Graph Neural Networks via Knowledge Distillation
F. E. Kelvinius* , D. Georgiev* , A. P. Toshev* , J. Gasteiger
NeurIPS 2023 / LOG 2023 (oral)
NeurIPS'23 / arXiv / poster / video @ LOG'23

Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks
A. P. Toshev, G. Galletti, J. Brandstetter, S. Adami, N. A. Adams
Geometric Science of Information (GSI) 2023
tweet / arXiv / poster / slides / code

E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
A. P. Toshev, G. Galletti, J. Brandstetter, S. Adami, N. A. Adams
Physics4ML workshop at ICLR 2023
⇾ workshop version of the GSI paper above.

On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods
A. P. Toshev, L. Paehler, A. Panizza, N. A. Adams
AI4Science workshop at ICML 2022
arXiv / poster / slides

Teaching

It was a pleasure to be a teaching assistant for the following courses at TUM.

  • Seminar AI for Science
    Summer '23, '24
    ⇾ New Master's level course
  • Introduction to Scientific Machine Learning for Engineers (lecture/exercise)
    Winter '22/23, '23/24, '24/25
    ⇾ Check out our SciML Jupyter Book, which I'm maintaining
  • Turbulent flows (exercise)
    Summer '22
  • Turbulent flow simulation on HPC systems (practical course)
    Winter '21/22


In addition, I was supervising students as a tutor during my Master's studies.

  • Summer 20 - Engineering Mechanics 2 (MSE)


Last update 12.08.2026


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