33 citations · 60 across the 11 of their papers we have counts for
11 papers
ActWorld: From Explorable to Interactive World Model via Action-Aware Memory
Zhexiao Xiong, Yizhi Song, Hao Kang +11
Interactive world models aim to simulate environment dynamics under real-time user actions. However, their action vocabulary is largely confined to navigation: most actions corresp…
Flux Already Knows -- Activating Subject-Driven Image Generation without Training
Hao Kang, Stathi Fotiadis, Liming Jiang +5
We propose a simple yet effective zero-shot framework for subject-driven image generation using a vanilla Flux model. By framing the task as grid-based image completion and simply…
Stochastic Flow Matching for Resolving Small-Scale Physics
Stathi Fotiadis, Noah Brenowitz, Tomas Geffner +4
Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolvin…
Score Normalization for a Faster Diffusion Exponential Integrator Sampler
Guoxuan Xia, Duolikun Danier, Ayan Das +4
Recently, Zhang et al. have proposed the Diffusion Exponential Integrator Sampler (DEIS) for fast generation of samples from Diffusion Models. It leverages the semi-linear nature o…
Image generation with shortest path diffusion
Ayan Das, Stathi Fotiadis, Anil Batra +5
The field of image generation has made significant progress thanks to the introduction of Diffusion Models, which learn to progressively reverse a given image corruption. Recently,…
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
Mario Lino, Stathi Fotiadis, Anil A. Bharath +1
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have…
Disentangled Generative Models for Robust Prediction of System Dynamics
Stathi Fotiadis, Mario Lino, Shunlong Hu +3
Deep neural networks have become increasingly of interest in dynamical system prediction, but out-of-distribution generalization and long-term stability still remains challenging.…
Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks
Mario Lino, Chris Cantwell, Anil A. Bharath +1
Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance…
Simulating Surface Wave Dynamics with Convolutional Networks
Mario Lino, Chris Cantwell, Stathi Fotiadis +2
We investigate the performance of fully convolutional networks to simulate the motion and interaction of surface waves in open and closed complex geometries. We focus on a U-Net ar…
Comparing recurrent and convolutional neural networks for predicting wave propagation
Stathi Fotiadis, Eduardo Pignatelli, Mario Lino Valencia +3
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering. In this work, we investigate the perf…
An Empirical Evaluation of Adversarial Robustness under Transfer Learning
Todor Davchev, Timos Korres, Stathi Fotiadis +2
In this work, we evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we st…