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A review on the distribution of relaxation times analysis: A powerful tool for process identification of electrochemical systems
Abstract The Distribution of Relaxation Times (DRT) analysis gained considerable attention
for its ability to reveal detailed information about complex electrochemical processes without …
for its ability to reveal detailed information about complex electrochemical processes without …
A survey of projection-based model reduction methods for parametric dynamical systems
Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …
a wide range of complex physical phenomena; however, the inherent large-scale nature of …
Projection-based model reduction: Formulations for physics-based machine learning
This paper considers the creation of parametric surrogate models for applications in science
and engineering where the goal is to predict high-dimensional output quantities of interest …
and engineering where the goal is to predict high-dimensional output quantities of interest …
Data-driven operator inference for nonintrusive projection-based model reduction
This work presents a nonintrusive projection-based model reduction approach for full
models based on time-dependent partial differential equations. Projection-based model …
models based on time-dependent partial differential equations. Projection-based model …
Control of port-Hamiltonian differential-algebraic systems and applications
We discuss the modelling framework of port-Hamiltonian descriptor systems and their use in
numerical simulation and control. The structure is ideal for automated network-based …
numerical simulation and control. The structure is ideal for automated network-based …
Machine learning for fast and reliable solution of time-dependent differential equations
We propose a data-driven Model Order Reduction (MOR) technique, based on Artificial
Neural Networks (ANNs), applicable to dynamical systems arising from Ordinary Differential …
Neural Networks (ANNs), applicable to dynamical systems arising from Ordinary Differential …
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
This work presents a non-intrusive model reduction method to learn low-dimensional
models of dynamical systems with non-polynomial nonlinear terms that are spatially local …
models of dynamical systems with non-polynomial nonlinear terms that are spatially local …
Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence
Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …
The shifted proper orthogonal decomposition: A mode decomposition for multiple transport phenomena
Transport-dominated phenomena provide a challenge for common mode-based model
reduction approaches. We present a model reduction method, which is suited for these kinds …
reduction approaches. We present a model reduction method, which is suited for these kinds …
[Књига][B] Interpolatory methods for model reduction
Dynamical systems are at the core of computational models for a wide range of complex
phenomena and, as a consequence, the simulation of dynamical systems has become a …
phenomena and, as a consequence, the simulation of dynamical systems has become a …