Publications
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
A. P. Toshev, J. A. Erbesdobler, N. A. Adams, J. Brandstetter
ICML 2024
⇾ project page / 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
⇾ 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
⇾ 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
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