Explainable machine learning in materials science
Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …
their exceptional accuracy. However, the most accurate machine learning models are …
Data-driven prediction in dynamical systems: recent developments
In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …
larger-scale systems in the majority of the grand societal challenges tackled in applied …
Lasdi: Parametric latent space dynamics identification
Enabling fast and accurate physical simulations with data has become an important area of
computational physics to aid in inverse problems, design-optimization, uncertainty …
computational physics to aid in inverse problems, design-optimization, uncertainty …
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …
A fast and accurate domain decomposition nonlinear manifold reduced order model
This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …
Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder
Numerically solving partial differential equations (PDEs) can be challenging and
computationally expensive. This has led to the development of reduced-order models …
computationally expensive. This has led to the development of reduced-order models …
Reduced order models for Lagrangian hydrodynamics
As a mathematical model of high-speed flow and shock wave propagation in a complex
multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes …
multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes …
Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking
This work introduces a new approach to reduce the computational cost of solving partial
differential equations (PDEs) with convection-dominated solutions: model reduction with …
differential equations (PDEs) with convection-dominated solutions: model reduction with …
Component-wise reduced order model lattice-type structure design
S McBane, Y Choi - Computer methods in applied mechanics and …, 2021 - Elsevier
Lattice-type structures can provide a combination of stiffness with light weight that is
desirable in a variety of applications. Design optimization of these structures must rely on …
desirable in a variety of applications. Design optimization of these structures must rely on …
gLaSDI: Parametric physics-informed greedy latent space dynamics identification
A parametric adaptive physics-informed greedy Latent Space Dynamics Identification
(gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order …
(gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order …