Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
[HTML][HTML] A review of artificial neural networks in the constitutive modeling of composite materials
Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …
to the widespread digital data, growing computing power, and advanced algorithms. The …
Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
Recently, a class of machine learning methods called physics-informed neural networks
(PINNs) has been proposed and gained prevalence in solving various scientific computing …
(PINNs) has been proposed and gained prevalence in solving various scientific computing …
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …
evaluate building performance. To this end, we leverage the recent advances in deep …
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element …
Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …
boundary value problem. Here, the training of the network is equivalent to the minimization …
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Data-driven modeling and learning in science and engineering
In the past, data in which science and engineering is based, was scarce and frequently
obtained by experiments proposed to verify a given hypothesis. Each experiment was able …
obtained by experiments proposed to verify a given hypothesis. Each experiment was able …
Physics-informed deep learning for computational elastodynamics without labeled data
Numerical methods such as finite element have been flourishing in the past decades for
modeling solid mechanics problems via solving governing partial differential equations …
modeling solid mechanics problems via solving governing partial differential equations …