Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
[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 …
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 …
Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling
The rise of machine learning has fueled the discovery of new materials and, especially,
metamaterials—truss lattices being their most prominent class. While their tailorable …
metamaterials—truss lattices being their most prominent class. While their tailorable …
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
We present the application of a class of deep learning, known as Physics Informed Neural
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
Networks (PINN), to inversion and surrogate modeling in solid mechanics. We explain how …
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 …
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Develo** accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …
is a long-standing problem. Recent advances in machine learning have revolutionized the …
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 …
Recent advances and applications of machine learning in experimental solid mechanics: A review
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Data-driven and physics-informed deep learning operators for solution of heat conduction equation with parametric heat source
Deep neural networks as universal approximators of partial differential equations (PDEs)
have attracted attention in numerous scientific and technical circles with the introduction of …
have attracted attention in numerous scientific and technical circles with the introduction of …
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 …