Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

no code implementations • 18 Jul 2023 • Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla, Rahul Babu Koneru, Zhen Li, Luis Bravo, Anindya Ghoshal, George Em Karniadakis, Mikko Karttunen

Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression.

Symbolic Regression

On the Parameter Combinations That Matter and on Those That do Not

no code implementations • 13 Oct 2021 • Nikolaos Evangelou, Noah J. Wichrowski, George A. Kevrekidis, Felix Dietrich, Mahdi Kooshkbaghi, Sarah McFann, Ioannis G. Kevrekidis

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.

Coarse-scale PDEs from fine-scale observations via machine learning

no code implementations • 12 Sep 2019 • Seungjoon Lee, Mahdi Kooshkbaghi, Konstantinos Spiliotis, Constantinos I. Siettos, Ioannis G. Kevrekidis

In this paper, we introduce a data-driven framework for the identification of unavailable coarse-scale PDEs from microscopic observations via machine learning algorithms.

BIG-bench Machine Learning Gaussian Processes