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Found 9 papers, 4 papers with code
Score Normalization for a Faster Diffusion Exponential Integrator Sampler
As a simple fix, we propose to instead reparameterise the score (at inference) by dividing it by the average absolute value of previous score estimates at that time step collected from offline high NFE generations.
Image generation with shortest path diffusion
We compute the shortest path according to this metric, and we show that it corresponds to a combination of image sharpening, rather than blurring, and noise deblurring.
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice.
Disentangled Generative Models for Robust Prediction of System Dynamics
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
Continuum mechanics simulators, numerically solving one or more partial differential equations, are essential tools in many areas of science and engineering, but their performance often limits application in practice.
Fully Convolutional Approach for Simulating Wave Dynamics
In this work, we investigate the performance of fully convolutional networks to predict the motion and interaction of surface waves in open and closed complex geometries.
Simulating Surface Wave Dynamics with Convolutional Networks
We investigate the performance of fully convolutional networks to simulate the motion and interaction of surface waves in open and closed complex geometries.
Comparing recurrent and convolutional neural networks for predicting wave propagation
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering.
An Empirical Evaluation of Adversarial Robustness under Transfer Learning
Specifically, we study the effects of using robust optimisation in the source and target networks.
