activity
20222024
most citedNeural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

34 citations · 47 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision

Hao Dong, Eleni Chatzi, Olga Fink

The task of open-set domain generalization (OSDG) involves recognizing novel classes within unseen domains, which becomes more challenging with multiple modalities as input. Existi…

astro-ph.IM2024

Exploring one giga electronvolt cosmic gamma rays with a Cherenkov plenoscope capable of recording atmospheric light fields, Part 1: Optics

Sebastian Achim Mueller, Spyridon Daglas, Axel Arbet Engels +6

Detecting cosmic gamma rays at high rates is the key to time-resolve the acceleration of particles within some of the most powerful events in the universe. Time-resolving the emiss…

cs.CV20237 cited

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

Hao Dong, Ismail Nejjar, Han Sun +2

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to…

stat.AP2023

Bayesian decision-theoretic model selection for monitored systems

Antonios Kamariotis, Eleni Chatzi

Engineers are often faced with the decision to select the most appropriate model for simulating the behavior of engineered systems, among a candidate set of models. Experimental mo…

math.NA2023

VpROM: A novel Variational AutoEncoder-boosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems

Thomas Simpson, Konstantinos Vlachas, Anthony Garland +2

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-…

cs.CE20236 cited

Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing

Gregory Duthé, Imad Abdallah, Sarah Barber +1

Sensing the fluid flow around an arbitrary geometry entails extrapolating from the physical quantities perceived at its surface in order to reconstruct the features of the surround…