Functionally graded porous structures: Analyses, performances, and applications – A Review

Thin-Walled Structures, Journal Year: 2023, Volume and Issue: 191, P. 111046 - 111046

Published: Aug. 10, 2023

Structural innovation incorporating bio-inspired composites poses a fresh angle to develop novel lightweight forms with strengthened mechanical properties, among which must-discuss topic is porous structures. The introduction of internal pores mimics the natural bones or timbers, makes density designable parameter, and opens new world for researchers engineers who have been obsessed in variety structural desired aspects. One important trends development functionally graded (FG) structures, where porosity gradations present significant potential further enhance already superior performances. This paper aimed review recent research advances this field by centring on adopted analysis approaches, obtained findings, application opportunities. We first elaborate general concepts FG as well corresponding forms. widely employed theoretical method subsequently looked at, touching nanofiller reinforcement followed details examples numerical modelling tests. related artificial intelligence (AI) assisted calculations are also discussed. fabrication techniques specimens, e.g. additive manufacturing (AM), foam, lattice, honeycomb based studies strategically categorised. later performance overview highlights advantages originated from non-uniform cellular morphologies overall buckling, bending, vibration, compressive energy absorption. Finally, perspectives various sectors future directions given. synopsis enables readers grab big picture structures possibly enlightens path outlook scope.

Language: Английский

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Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2022, Volume and Issue: 405, P. 115852 - 115852

Published: Dec. 28, 2022

Language: Английский

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Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity

Neural Networks, Journal Year: 2023, Volume and Issue: 162, P. 472 - 489

Published: March 13, 2023

Language: Английский

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Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Journal of Computing and Information Science in Engineering, Journal Year: 2024, Volume and Issue: 24(4)

Published: Jan. 8, 2024

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep push scientific forward a range of disciplines, such as fluid mechanics, solid materials science, etc. The incorporation neural networks is particularly crucial this hybridization process. Due their intrinsic architecture, conventional cannot be successfully trained scoped when data are sparse, which the case many engineering domains. Nonetheless, provide foundation respect physics-driven or knowledge-based constraints during training. Generally speaking, there three distinct network frameworks enforce underlying physics: (i) physics-guided (PgNNs), (ii) physics-informed (PiNNs), (iii) physics-encoded (PeNNs). These methods advantages for accelerating numerical modeling complex multiscale multiphysics phenomena. In addition, recent developments operators (NOs) add another dimension these new simulation paradigms, especially real-time prediction systems required. All models also come with own unique drawbacks limitations that call further fundamental research. This study aims present review four (i.e., PgNNs, PiNNs, PeNNs, NOs) used state-of-the-art architectures applications reviewed, discussed, future research opportunities presented terms improving algorithms, considering causalities, expanding applications, coupling solvers.

Language: Английский

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A deep learning energy-based method for classical elastoplasticity

International Journal of Plasticity, Journal Year: 2023, Volume and Issue: 162, P. 103531 - 103531

Published: Jan. 20, 2023

Language: Английский

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Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

Thin-Walled Structures, Journal Year: 2024, Volume and Issue: 205, P. 112495 - 112495

Published: Sept. 24, 2024

Language: Английский

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Theory and implementation of inelastic Constitutive Artificial Neural Networks

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 428, P. 117063 - 117063

Published: June 4, 2024

The two fundamental concepts of materials theory, pseudo potentials and the assumption a multiplicative decomposition, allow general description inelastic material behavior. increase in computer performance enabled us to thoroughly investigate predictive capabilities ever more complex choices for potential Helmholtz free energy. Today, however, we have reached point where their models are becoming increasingly sophisticated. This raises question: How do find best model that includes all effects explain our data? Constitutive Artificial Neural Networks (CANN) may answer this question. Here, extend CANNs (iCANN). Rigorous considerations objectivity, rigid motion reference configuration, decomposition its inherent non-uniqueness, choice appropriate stretch tensors, restrictions energy potential, consistent evolution guide towards architecture iCANN satisfying thermodynamics per design. We combine feed-forward networks with recurrent neural network approach take time dependencies into account. Specializing visco-elasticity, demonstrate is capable autonomously discovering artificially generated data, response polymers at different rates cyclic loading as well relaxation behavior muscle data. Since design not limited iCANNs might help identify phenomena subsequently select most model. focus on providing thermodynamically framework behaviors how incorporate an architecture-based manner. Our source code, examples available Holthusen et al. (2023a) ( https://doi.org/10.5281/zenodo.10066805).

Language: Английский

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A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems

Multiscale Science and Engineering, Journal Year: 2024, Volume and Issue: 6(1), P. 1 - 11

Published: Feb. 13, 2024

Language: Английский

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Differentiable finite element method with Galerkin discretization for fast and accurate inverse analysis of multidimensional heterogeneous engineering structures

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117755 - 117755

Published: Jan. 22, 2025

Language: Английский

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Transfer Learning-Enhanced Finite Element-Integrated Neural Networks

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110075 - 110075

Published: Feb. 1, 2025

Language: Английский

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