Open AccessAcceleration of PDE-Based Biological Simulation Through the Development of Neural Network Metamodels
Author(s) -
Lukasz Burzawa,
Linlin Li,
Xu Wang,
Adrián Buganza Tepole,
David M. Umulis
Publication year - 2020
Publication title -
current pathobiology reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.177
H-Index - 24
ISSN - 2167-485X
DOI - 10.1007/s40139-020-00216-8
Subject(s) - computer science , sensitivity (control systems) , artificial neural network , inference , traverse , parametric statistics , partial differential equation , optimization problem , mathematical optimization , artificial intelligence , machine learning , algorithm , mathematics , mathematical analysis , statistics , geodesy , electronic engineering , engineering , geography
Partial differential equation (PDE) mathematical models of biological systems and the simulation approaches used to solve them are widely used to test hypotheses and infer regulatory interactions based on optimization of the PDE model against the observed data. In this review, we discuss the ability of powerful machine learning methods to accelerate the parametric screening of biophysical informed- PDE systems.