Multiscale graph neural network autoencoders for interpretable scientific machine learning
The goal of this work is to address two limitations in autoencoder-based models: latent
space interpretability and compatibility with unstructured meshes. This is accomplished here …
space interpretability and compatibility with unstructured meshes. This is accomplished here …
A generative adversarial network (GAN) approach to creating synthetic flame images from experimental data
Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of
reacting flows; however, they tend to generate massive data-sets, rendering conventional …
reacting flows; however, they tend to generate massive data-sets, rendering conventional …
PIV Snapshot Clustering Reveals the Dual Deterministic and Chaotic Nature of Propeller Wakes at Macro-and Micro-Scales
This study investigates the underlying mechanisms governing the evolution of tip vortices in
the far field of a naval propeller wake. To achieve this, a novel approach utilizing data …
the far field of a naval propeller wake. To achieve this, a novel approach utilizing data …
Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows
This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physics-
informed clustering strategy centered on the K-means framework. The method allows for the …
informed clustering strategy centered on the K-means framework. The method allows for the …
Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches
Combustion instability in gas turbines and rocket engines, as one of the most challenging
problems in combustion research, arises from the complex interactions among flames …
problems in combustion research, arises from the complex interactions among flames …
Cluster regression model for flow control
In the realm of big data, discerning patterns in nonlinear systems affected by external control
inputs is increasingly challenging. Our approach blends the coarse-graining strengths of …
inputs is increasingly challenging. Our approach blends the coarse-graining strengths of …
Understanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems
The neural ordinary differential equation (ODE) framework has shown promise in
developing accelerated surrogate models for complex systems described by partial …
developing accelerated surrogate models for complex systems described by partial …
Cluster regression model for control of nonlinear dynamics
N Arya, AG Nair - arXiv preprint arXiv:2312.14186, 2023 - arxiv.org
In the realm of big data, discerning patterns in nonlinear systems affected by external control
inputs is increasingly challenging. Our approach blends the coarse-graining strengths of …
inputs is increasingly challenging. Our approach blends the coarse-graining strengths of …
Observing piv measurements through the lens of data clustering
Spatial and snapshot clustering approaches are presented and discussed for particle image
velocimetry (PIV) data of high-Reynolds number uniform and buoyant jets and 4-and 7 …
velocimetry (PIV) data of high-Reynolds number uniform and buoyant jets and 4-and 7 …
A lipschitzian global optimization algorithm and machine learning for fluid dynamics
D D'Agostino - 2021 - iris.uniroma1.it
The research conducted and resumed in this thesis covers two different topics. In chapter 1, I
focused my research on the development of a new Global Optimization algorithm informed …
focused my research on the development of a new Global Optimization algorithm informed …