33 citations · 87 across the 20 of their papers we have counts for
22 papers
Structure-preserving fidelity diagnostics for partial differential equations
Kyriakos Flouris
Machine learning surrogates of partial differential equations (PDEs) are commonly trained on data generated by numerical solvers. Therefore, the training data obey not the PDE but…
Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting
Tamanna Kumavat, Georg Brunner, Kyriakos Flouris
We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework, with the goal of understandin…
GENIE: Generative Neural Inference for Epidemics
Laura M. Guzmán-Rincón, George R. E. Bradley, Joel Kandiah +5
The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epid…
Diffusion Path Alignment for Long-Range Motion Generation and Domain Transitions
Haichao Wang, Alexander Okupnik, Yuxing Han +3
Long-range human movement generation remains a central challenge in computer vision and graphics. Generating coherent transitions across semantically distinct motion domains remain…
Generative human motion mimicking through feature extraction in denoising diffusion settings
Alexander Okupnik, Johannes Schneider, Kyriakos Flouris
Recent success with large language models has sparked a new wave of verbal human-AI interaction. While such models support users in a variety of creative tasks, they lack the embod…
Operator Flow Matching for Timeseries Forecasting
Yolanne Yi Ran Lee, Kyriakos Flouris
Forecasting high-dimensional, PDE-governed dynamics remains a core challenge for generative modeling. Existing autoregressive and diffusion-based approaches often suffer cumulative…
Hyperbolic Optimization
Yanke Wang, Kyriakos Flouris
This work explores optimization methods on hyperbolic manifolds. Building on Riemannian optimization principles, we extend the Hyperbolic Stochastic Gradient Descent (a specializat…
Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI
Kyriakos Flouris, Moritz Halter, Yolanne Y. R. Lee +6
Hemodynamic analysis is essential for predicting aneurysm rupture and guiding treatment. While magnetic resonance flow imaging enables time-resolved volumetric blood velocity measu…
A Multi-Centric Anthropomorphic 3D CT Phantom-Based Benchmark Dataset for Harmonization
Mohammadreza Amirian, Michael Bach, Oscar Jimenez-del-Toro +18
Artificial intelligence (AI) has introduced numerous opportunities for human assistance and task automation in medicine. However, it suffers from poor generalization in the presenc…
A purely Quantum Generative Modeling through Unitary Scrambling and Collapse
Yihua Li, Jiayi Chen, Tamanna S. Kumavat +1
Quantum computing offers fundamentally more expressive mechanisms for generative modeling, yet current approaches remain constrained by classical neural components that bottleneck…
Explicit and data-Efficient Encoding via Gradient Flow
Kyriakos Flouris, Anna Volokitin, Gustav Bredell +1
The autoencoder model typically uses an encoder to map data to a lower dimensional latent space and a decoder to reconstruct it. However, relying on an encoder for inversion can le…
Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI
Yanke Wang, Yolanne Y. R. Lee, Aurelio Dolfini +3
Lumbar spine problems are ubiquitous, motivating research into targeted imaging for treatment planning and guided interventions. While high resolution and high contrast CT has been…
A Matrix Product State Model for Simultaneous Classification and Generation
Alex Mossi, Bojan Žunkovic, Kyriakos Flouris
Quantum machine learning (QML) is a rapidly expanding field that merges the principles of quantum computing with the techniques of machine learning. One of the powerful mathematica…
Canonical normalizing flows for manifold learning
Kyriakos Flouris, Ender Konukoglu
Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the hig…
Explicitly Minimizing the Blur Error of Variational Autoencoders
Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya +2
Variational autoencoders (VAEs) are powerful generative modelling methods, however they suffer from blurry generated samples and reconstructions compared to the images they have be…
Frobenius integrability of certain -forms on singular spaces
Junyan Cao, Andreas Höring
Demailly proved that on a smooth compact Kähler manifold the distribution defined by a holomorphic -form with values in an anti-pseudoeffective line bundle is always integrable.…
Gradient flow encoding with distance optimization adaptive step size
Kyriakos Flouris, Anna Volokitin, Gustav Bredell +1
The autoencoder model uses an encoder to map data samples to a lower dimensional latent space and then a decoder to map the latent space representations back to the data space. Imp…
Quantized alternate current on curved graphene
Kyriakos Flouris, Sauro Succi, Hans J. Herrmann
Based on the numerical solution of the quantum lattice Boltzmann method in curved space, we predict the onset of a quantized alternating current on curved graphene sheets. Such num…
Curvature-induced quantum spin-Hall effect on a Möbius strip
Kyriakos Flouris, Miller Mendoza Jimenez, Hans J. Herrmann
The quantum Hall effect has been predicted and discovered in various condensed-matter systems. A promising quantum material for such topological effects is graphene. We report the…
Landau levels in wrinkled and rippled graphene sheets
Kyriakos Flouris, Miller Mendoza, Hans J. Herrmann
We study the discrete energy spectrum of curved graphene sheets in the presence of a magnetic field. The shifting of the Landau levels is determined for complex and realistic geome…
Confining massless Dirac particles in two-dimensional curved space
Kyriakos Flouris, Miller Mendoza Jimenez, Jens-Daniel Debus +1
Dirac particles have been notoriously difficult to confine. Implementing a curved space Dirac equation solver based on the quantum Lattice Boltzmann method, we show that curvature…
Fluid structure interaction with curved space lattice Boltzmann
Kyriakos Flouris, Miller Mendoza Jimenez, Gautam Munglani +3
We present a novel method for fluid structure interaction (FSI) simulations where an original 2nd-order curved space lattice Boltzmann fluid solver (LBM) is coupled to a finite ele…