72 citations · 144 across the 7 of their papers we have counts for
8 papers
Borel Summability of the Spatial Gradient Expansion in Linear Kinetic Theory
Mahdi Kooshkbaghi
The passage from kinetic theory to hydrodynamics proceeds through gradient expansions whose large-order behavior depends on the model and on whether the gradients are temporal or s…
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla +6
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of…
Machine learning based data-driven discovery of nonlinear phase-field dynamics
Elham Kiyani, Steven Silber, Mahdi Kooshkbaghi +1
One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coa…
On the Parameter Combinations That Matter and on Those That do Not
Nikolaos Evangelou, Noah J. Wichrowski, George A. Kevrekidis +4
We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diff…
Emergent spaces for coupled oscillators
Thomas N. Thiem, Mahdi Kooshkbaghi, Tom Bertalan +2
In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the…
Coarse-scale PDEs from fine-scale observations via machine learning
Seungjoon Lee, Mahdi Kooshkbaghi, Konstantinos Spiliotis +2
Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through e.g. atomistic, agent-based or lattice models) based on first princip…
Manifold Learning for Organizing Unstructured Sets of Process Observations
Felix Dietrich, Mahdi Kooshkbaghi, Erik M. Bollt +1
Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In t…
Manifold learning for parameter reduction
Alexander Holiday, Mahdi Kooshkbaghi, Juan M. Bello-Rivas +3
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that re…