output
20152025
most citedFrom coarse wall measurements to turbulent velocity fields through deep learning

132 citations

10 papers

cs.AR2025★ 4 cited

High-Level Synthesis of Digital Circuits from Template Haskell and SDF-AP

Hendrik Folmer, Robert de Groote, Marco Bekooij

Functional languages as input specifications for High-Level Synthesis (HLS) tools allow to specify data dependencies but do not contain a notion of time nor execution order. In thi…

cs.LG2025★ 23 cited

Humanity's Last Exam

Long Phan, Alice Gatti, Ziwen Han +1168

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…

cs.RO2023★ 11 cited

Safe Physical Human-Robot Interaction through Variable Impedance Control based on ISO/TS 15066

Armin Ghanbarzadeh, Esmaeil Najafi

The successful implementation of Physical Human-Robot Interaction in industrial environments depends on ensuring safe collaboration between human operators and robotic devices. Thi…

cond-mat.mtrl-sci2022★ 2 cited

Kinetics and the crystallographic structure of bismuth during liquefaction and solidification on the insulating substrate

Tjeerd R. J. Bollmann, Maciej Jankowski

Here we study the kinetics of liquefaction and solidification of thin bismuth films grown on the insulating substrate by the pulsed laser deposited (PLD) and molecular beam epitaxy…

cs.RO2021★ 4 cited

Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning

Nicolò Botteghi, Luuk Grefte, Mannes Poel +4

Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, t…

cs.LG2021★ 1 cited

Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics

Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1

Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…

physics.flu-dyn2021★ 132 cited

From coarse wall measurements to turbulent velocity fields through deep learning

Alejandro Güemes, Stefano Discetti, Andrea Ianiro +3

This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse…

cs.LG2020★ 4 cited

Low Dimensional State Representation Learning with Reward-shaped Priors

Nicolò Botteghi, Ruben Obbink, Daan Geijs +5

Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal polic…

physics.flu-dyn2018★ 19 cited

The liquid helix

E. Jambon-Puillet, W. Bouwhuis, J. H. Snoeijer +1

From everyday experience, we all know that a solid edge can deflect a liquid flowing over it significantly, up to the point where the liquid completely sticks to the solid. Althoug…

quant-ph2015★ 72 cited

Programmable two-photon quantum interference in channels in opaque scattering media

Tom A. W. Wolterink, Ravitej Uppu, Georgios Ctistis +3

We investigate two-photon quantum interference in an opaque scattering medium that intrinsically supports transmission channels. By adaptive spatial phase-modulation of the…