[HTML][HTML] Machine learning aided nanoindentation: A review of the current state and future perspectives
The solution of instrumented indentation inverse problems by physically-based models still
represents a complex challenge yet to be solved in metallurgy and materials science. In …
represents a complex challenge yet to be solved in metallurgy and materials science. In …
Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …
and deep learning to advance scientific computing in many fields, including fluid mechanics …
A deep learning approach for inverse design of gradient mechanical metamaterials
Mechanical metamaterials with unique micro-architectures possess excellent physical
properties in terms of stiffness, toughness, vibration isolation, and thermal expansion …
properties in terms of stiffness, toughness, vibration isolation, and thermal expansion …
A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities
H Li, Z Zhang, T Li, X Si - Mechanical Systems and Signal Processing, 2024 - Elsevier
Remaining useful life (RUL) prediction, known as 'prognostics', has long been recognized as
one of the key technologies in prognostics and health management (PHM) to maintain the …
one of the key technologies in prognostics and health management (PHM) to maintain the …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Digital twin modeling for structural strength monitoring via transfer learning-based multi-source data fusion
Experimental measurement and numerical simulation are two typical methods to monitor the
strength variation of structures. However, the former method is difficult to lay sufficient …
strength variation of structures. However, the former method is difficult to lay sufficient …
Solving elastodynamics via physics-informed neural network frequency domain method
Despite the fact that physics-informed neural networks (PINN) have been developed rapidly
in recent years, their inherent spectral bias makes it difficult to approximate multi-frequency …
in recent years, their inherent spectral bias makes it difficult to approximate multi-frequency …
Transfer learning-based crashworthiness prediction for the composite structure of a subway vehicle
Due to the lack of load/displacement sensors in a complex and uncertain crash test/accident
of rail vehicles (eg, vehicle-to-vehicle or train-to-train collision), only structural deformation …
of rail vehicles (eg, vehicle-to-vehicle or train-to-train collision), only structural deformation …
Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains
Physics‐informed neural networks (PINNs) are a new tool for solving boundary value
problems by defining loss functions of neural networks based on governing equations …
problems by defining loss functions of neural networks based on governing equations …
Physics-informed neural network frameworks for crack simulation based on minimized peridynamic potential energy
L Ning, Z Cai, H Dong, Y Liu, W Wang - Computer Methods in Applied …, 2023 - Elsevier
Physics-informed neural networks (PINNs), which are promising tools for solving nonlinear
equations in the absence of labeled data, have been successfully applied for continuum …
equations in the absence of labeled data, have been successfully applied for continuum …