Aykut Argun
Aykut Argun
Modelling & AI Expert at Syntronic AB
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Cited by
Cited by
Experimental realization of a minimal microscopic heat engine
A Argun, J Soni, L Dabelow, S Bo, G Pesce, R Eichhorn, G Volpe
Physical Review E 96 (5), 052106, 2017
Non-Boltzmann stationary distributions and nonequilibrium relations in active baths
A Argun, AR Moradi, E Pinçe, GB Bagci, A Imparato, G Volpe
Physical Review E 94 (6), 062150, 2016
Objective comparison of methods to decode anomalous diffusion
G Muñoz-Gil, G Volpe, MA Garcia-March, E Aghion, A Argun, CB Hong, ...
Nature communications 12 (1), 6253, 2021
Digital video microscopy enhanced by deep learning
S Helgadottir, A Argun, G Volpe
Optica 6 (4), 506-513, 2019
Quantitative digital microscopy with deep learning
B Midtvedt, S Helgadottir, A Argun, J Pineda, D Midtvedt, G Volpe
Applied Physics Reviews 8 (1), 011310, 2021
Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
A Argun, G Volpe, S Bo
Journal of Physics A: Mathematical and Theoretical 54 (29), 294003, 2021
Enhanced force-field calibration via machine learning
A Argun, T Thalheim, S Bo, F Cichos, G Volpe
Applied Physics Reviews 7 (4), 041404, 2020
Roadmap for optical tweezers
G Volpe, OM Maragò, H Rubinzstein-Dunlop, G Pesce, AB Stilgoe, ...
arXiv preprint arXiv:2206.13789, 2022
Experimental evidence of the failure of Jarzynski equality in active baths
A Argun, AR Moradi, E Pince, GB Bagci, G Volpe
arXiv preprint arXiv:1601.01123, 2016
Simulation of Complex Systems
A Argun, A Callegari, G Volpe
IOP Publishing, 2021
Better stability with measurement errors
A Argun, G Volpe
Journal of Statistical Physics 163, 1477-1485, 2016
Playing with active matter
AB Balda, A Argun, A Callegari, G Volpe
arXiv preprint arXiv:2209.04168, 2022
Roadmap on Deep Learning for Microscopy
G Volpe, C Wählby, L Tian, M Hecht, A Yakimovich, K Monakhova, ...
ArXiv, 2023
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