77 citations · 87 across the 9 of their papers we have counts for
10 papers
Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling
Javier E. Santos, Agnese Marcato, Roman Colman +2
Generative diffusion models have achieved remarkable success in producing high-quality images. However, these models typically operate in continuous intensity spaces, diffusing ind…
Accelerating Multiphase Flow Simulations with Denoising Diffusion Model Driven Initializations
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +4
This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics-based simulations, aiming at reducing the computational costs of f…
Generating Multiphase Fluid Configurations in Fractures using Diffusion Models
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +3
Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen…
Using Ornstein-Uhlenbeck Process to understand Denoising Diffusion Probabilistic Model and its Noise Schedules
Javier E. Santos, Yen Ting Lin
The aim of this short note is to show that Denoising Diffusion Probabilistic Model DDPM, a non-homogeneous discrete-time Markov process, can be represented by a time-homogeneous co…
Data-Driven Mori-Zwanzig: Reduced Order Modeling of Sparse Sensors Measurements for Boundary Layer Transition
Michael Woodward, Yifeng Tian, Yen Ting Lin +5
Understanding, predicting and controlling laminar-turbulent boundary-layer transition is crucial for the next generation aircraft design. However, in real flight experiments, or wi…
Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces
Javier E Santos, Zachary R. Fox, Nicholas Lubbers +1
Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise.…
Data-Driven Mori-Zwanzig: Approaching a Reduced Order Model for Hypersonic Boundary Layer Transition
Michael Woodward, Yifeng Tian, Arvind Mohan +5
In this work, we apply, for the first time to spatially inhomogeneous flows, a recently developed data-driven learning algorithm of Mori-Zwanzig (MZ) operators, which is based on a…
A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks
Xiaoyu Ma, Sylvain Sardy, Nick Hengartner +2
To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability…
What needles do sparse neural networks find in nonlinear haystacks
Sylvain Sardy, Nicolas W Hengartner, Nikolai Bonenko +1
Using a sparsity inducing penalty in artificial neural networks (ANNs) avoids over-fitting, especially in situations where noise is high and the training set is small in comparison…
The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated
Steven Sanche, Yen Ting Lin, Chonggang Xu +3
The novel coronavirus (2019-nCoV) is a recently emerged human pathogen that has spread widely since January 2020. Initially, the basic reproductive number, R0, was estimated to be…