Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …
providing an expressive view of the chemical space and multiscale processes. Their …
It's about time: Linking dynamical systems with human neuroimaging to understand the brain
Most human neuroscience research to date has focused on statistical approaches that
describe stationary patterns of localized neural activity or blood flow. While these patterns …
describe stationary patterns of localized neural activity or blood flow. While these patterns …
[HTML][HTML] Data-driven surrogates of rotating detonation engine physics with neural ordinary differential equations and high-speed camera footage
J Koch - Physics of Fluids, 2021 - pubs.aip.org
Interacting multi-scale physics in the Rotating Detonation Engine (RDE) lead to diverse
nonlinear dynamical behavior, including combustion wave mode-locking, modulation, and …
nonlinear dynamical behavior, including combustion wave mode-locking, modulation, and …
Data-driven identification of 2D partial differential equations using extracted physical features
K Meidani, AB Farimani - Computer Methods in Applied Mechanics and …, 2021 - Elsevier
Many scientific phenomena are modeled by Partial Differential Equations (PDEs). The
development of data gathering tools along with the advances in machine learning (ML) …
development of data gathering tools along with the advances in machine learning (ML) …
Modal decomposition of flow data via gradient-based transport optimization
In the context of model reduction, we study an optimization problem related to the
approximation of given data by a linear combination of transformed modes, called …
approximation of given data by a linear combination of transformed modes, called …
Unveiling advection–dominated interactions: Efficacy of neural networks in natural systems modelling
Studying the interactions between advection and dispersion in natural systems, especially in
cases where advection predominates, are important because it is necessary to accurately …
cases where advection predominates, are important because it is necessary to accurately …
A discretization-free deep neural network-based approach for advection-dispersion-reaction mechanisms
This study aims to provide insights into new areas of artificial intelligence approaches by
examining how these techniques can be applied to predict behaviours for difficult physical …
examining how these techniques can be applied to predict behaviours for difficult physical …
Decomposition of flow data via gradient-based transport optimization
We study an optimization problem related to the approximation of given data by a linear
combination of transformed modes. In the simplest case, the optimization problem reduces …
combination of transformed modes. In the simplest case, the optimization problem reduces …
Sparse system identification by low-rank approximation
F Vides - arXiv preprint arXiv:2105.07522, 2021 - arxiv.org
In this document, some general results in approximation theory and matrix analysis with
applications to sparse identification of time series models and nonlinear discrete-time …
applications to sparse identification of time series models and nonlinear discrete-time …
[PDF][PDF] A STUDY OF PREDOMINANT ADVECTION WITH DISPERSIVITY USING DEEP NEURAL NETWORKS
Exploring the dynamics between advection and dispersion in environments such as
hydrology and atmospheric science is crucial for precise physical modeling. Traditional …
hydrology and atmospheric science is crucial for precise physical modeling. Traditional …