[HTML][HTML] A review and perspective on hybrid modelling methodologies
The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …
from knowledge about the system) and nonparametric models (typically deduced from data) …
The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biology
B Noordijk, ML Garcia Gomez… - Frontiers in Systems …, 2024 - frontiersin.org
Both machine learning and mechanistic modelling approaches have been used
independently with great success in systems biology. Machine learning excels in deriving …
independently with great success in systems biology. Machine learning excels in deriving …
Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations
Despite rapid progress in data acquisition techniques, many complex physical, chemical,
and biological systems remain only partially observable, thus posing the challenge to …
and biological systems remain only partially observable, thus posing the challenge to …
Generalized Inverse Optimal Control and its Application in Biology
Living organisms exhibit remarkable adaptations across all scales, from molecules to
ecosystems. We believe that many of these adaptations correspond to optimal solutions …
ecosystems. We believe that many of these adaptations correspond to optimal solutions …
Parameter Dependence in Identifiability Applied to FP-Fisher-KPP Reaction-Diffusion Equations Parameterized for Tauopathy Network Modeling
A Rahimabadi, H Benali - bioRxiv, 2024 - biorxiv.org
Parameterization and a priori identifiability analysis are two interconnected steps that should
be carried out in advance of model calibration. In the first place, we propose a framework for …
be carried out in advance of model calibration. In the first place, we propose a framework for …
Inference from Limited Observations in Statistical, Dynamical, and Functional Problems
G Stepaniants - 2024 - dspace.mit.edu
Observational data in physics and the life sciences comes in many varieties. Broadly, we
can divide datasets into cross-sectional data which record a set of observations at a given …
can divide datasets into cross-sectional data which record a set of observations at a given …
[PDF][PDF] Digital Chemical Engineering
The term hybrid modeling refers to the combination of parametric models (typically derived
from knowledge about the system) and nonparametric models (typically deduced from data) …
from knowledge about the system) and nonparametric models (typically deduced from data) …