[HTML][HTML] Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Reinforced composite is a preferred choice of material for the design of industrial lightweight
structures. As of late, composite materials analysis and development utilizing machine …
structures. As of late, composite materials analysis and development utilizing machine …
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 …
[HTML][HTML] Advances and opportunities in high-throughput small-scale mechanical testing
The quest for novel materials used in technologies demanding extreme performance has
been accelerated by advances in computational materials screening, additive manufacturing …
been accelerated by advances in computational materials screening, additive manufacturing …
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Developing 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 …
Learning the stress-strain fields in digital composites using Fourier neural operator
Increased demands for high-performance materials have led to advanced composite
materials with complex hierarchical designs. However, designing a tailored material …
materials with complex hierarchical designs. However, designing a tailored material …
[HTML][HTML] Indentation, finite element modeling and artificial neural network studies on mechanical behavior of GFRP composites in an acidic environment
In this study, the indentation tests are performed with various forces using the Vickers
indenter to investigate the mechanical properties, including the elastic modulus, hardness …
indenter to investigate the mechanical properties, including the elastic modulus, hardness …
[HTML][HTML] Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning
Hybrid composites combine two or more different fillers to achieve multifunctional or
advanced material properties, such as lightweight and enhanced mechanical properties …
advanced material properties, such as lightweight and enhanced mechanical properties …
Data-driven methods for stress field predictions in random heterogeneous materials
Predicting full-field stress responses is of fundamental importance to assessing materials
failure and has various engineering applications in design optimization, manufacturing …
failure and has various engineering applications in design optimization, manufacturing …
[HTML][HTML] StressD: 2D Stress estimation using denoising diffusion model
Finite element analysis (FEA), a common approach for simulating stress distribution for a
given geometry, is generally associated with high computational cost, especially when high …
given geometry, is generally associated with high computational cost, especially when high …
A machine learning framework for intelligent development of Ultra-High performance concrete (UHPC): From dataset cleaning to performance predicting
L Xu, D Fan, K Liu, W Xu, R Yu - Expert Systems with Applications, 2024 - Elsevier
This study proposes a new machine learning (ML) framework, which mainly includes dataset
cleaning processing as well as performance predicting, for property prediction of ultra-high …
cleaning processing as well as performance predicting, for property prediction of ultra-high …